Survey of energy-efficient solutions in NetworkOlivier Zheng
Since 2000, the growth of IT has increased in a radical way, to become the first carbon dioxide producer. It is at this time that people start to think about the global warming and the bad effect of the carbon dioxide. Everybody try to reduce his carbon dioxide footprint. Today, computers are connected to a network. So the main actors in IT (manufacturers, organisations...) start to work on solution to decrease the Network’s footprint.
In this paper, different non-commercial energy-efficient solutions are described: the 802.3az standard, the EMAN framework, the energy-efficiency metrics and the MiDORi network. Each solution is focused on a particular part of the Network (from the interface, to the global design), it has its advantages, but it has also its disadvantages. We’ll talk more about the disadvantages of each solution.
Each solution introduced in this paper will decrease the power consumption of the network. But to have the best results, the chosen solution will have to be implemented in the good way to have the best impact on the network power consumption: having Core layer routers with 802.3az ports won’t save more energy than having Access layer switches with 802.3az ports.
Brief look at staus of Climate Change and impacts from/into ICTs. Identify the roles of NGN to mitigate climate change and prposed new model of ntwork architecture.
Energy Efficient Wireless CommunicationsJingon Joung
The tutorial begins with a brief introduction of spectral efficiency (SE) and energy efficiency (EE) in communications. Various definitions of EE will also be covered briefly. One typically used EE, i.e., bits-per-Joule, is theoretically derived and the ideal SE-EE tradeoff is introduced. It is recognized that a power amplifier (PA) is one of the most critical components in wireless communication systems and consumes a significant fraction of the total energy. The fundamental properties of PA, such as linearity and efficiency, are introduced. With the practical characteristics of PA, the detrimental effects of the signal non-linearity and power inefficiency of the PA on the SE, EE, and their tradeoff, are quantified.
Next, various existing EE-improving techniques are categorized from three perspectives: PA design, signal design and network design. This broad understanding based on the three categories will help motivate holistic design approaches to mitigate the non-ideal effects in real-life PA devices, and accelerate cross-domain research to further enhance the available techniques for high EE or good SE-EE tradeoff.
Last, the remaining challenges and future work for EE issue will be discussed.
Performance Analysis of Antenna Selection Techniques in MIMO-OFDM System with...IJECEIAES
In this paper we propose a new MIMO-OFDM model in which we consider various antenna selection techniques like Bulk selection and Per-subcarrier selection etc. with hardware impairments such as non-linearties of amplifiers, quantization noise, phase noise and I-Q imbalance etc. As we know that the transceiver hardware impairments limit the channel capacity and the energy efficiency of MIMO-OFDM system, so we can not neglect the fundamental impacts of these hardware impairments {Kappa (0.05 0.1)} on the energy efficiency in the high SNR domain. Therefore we analyze the Energy Efficiency of Bulk and Per-subcarrier antenna selection techniques with or without hardware impairments. It has been observed that the energy efficiency decreases as the value of these hardware impairments increases. As we compared the Bulk antenna selection with the Per-subcarrier antenna selection scheme, the Per-subcarrier antenna selection requires more number of RF (radio frequency) chains and transmits power in comparison to the Bulk selection. Due to this, the Bulk antenna selection technique is more energy efficient than Per-subcarrier antenna selection.
NETWORK ENERGY SAVING TECHNOLOGIES FOR GREEN WIRELESS ACCESS NETWORKSMadhav Thekkedath
Now a day smart phone users are increased and the energy consumption problem in mobile industry has become crucial. For the growth of mobile industry energy efficiency of the system must be improved. In order to improve the methods of network energy usage usually reduction of the Base Stations are to be employed.
The mobile industry faces a critical energy consumption challenge. By 2014 smart phones will exceed 1.82 billion units and surpass PCs as the most common web access devices. That’s why it is one of the important things. As all know that a smart is mainly for easy access to the network at anytime and anywhere. So it employs powering up all the Radio Base Stations (RBS’s) at maximum power. Since there are more number of service providers competition exist at the field of telecommunication. So it yields in more energy consumption to get monopoly among them. In the field of engineering, a system is usually designed to transform energy to useful work. Energy Efficiency can therefore be defined as the ratio of useful work to the total supplied energy. The useful work in a communication system refers to the effort to deliver modulated signals for information exchange. The definition of Energy Efficiency varies according to measured objects. There are two basic methods to measure Energy Efficiency. One way is to define Energy Efficiency as the ratio of efficient output power/energy to total input power/energy. This definition is widely used by systems and components such as power supply, Power Amplifiers, and antennas. The other way defines Energy Efficiency as the performance per unit of energy consumption.
The seminar gives a brief description about the methods to manage the network energy even when the on time access to network by mobile phones. Also gives a summary about existing and latest methodologies about the management in RBS’s.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Influence choice of the injection nodes of energy source on on-line losses of...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Survey of energy-efficient solutions in NetworkOlivier Zheng
Since 2000, the growth of IT has increased in a radical way, to become the first carbon dioxide producer. It is at this time that people start to think about the global warming and the bad effect of the carbon dioxide. Everybody try to reduce his carbon dioxide footprint. Today, computers are connected to a network. So the main actors in IT (manufacturers, organisations...) start to work on solution to decrease the Network’s footprint.
In this paper, different non-commercial energy-efficient solutions are described: the 802.3az standard, the EMAN framework, the energy-efficiency metrics and the MiDORi network. Each solution is focused on a particular part of the Network (from the interface, to the global design), it has its advantages, but it has also its disadvantages. We’ll talk more about the disadvantages of each solution.
Each solution introduced in this paper will decrease the power consumption of the network. But to have the best results, the chosen solution will have to be implemented in the good way to have the best impact on the network power consumption: having Core layer routers with 802.3az ports won’t save more energy than having Access layer switches with 802.3az ports.
Brief look at staus of Climate Change and impacts from/into ICTs. Identify the roles of NGN to mitigate climate change and prposed new model of ntwork architecture.
Energy Efficient Wireless CommunicationsJingon Joung
The tutorial begins with a brief introduction of spectral efficiency (SE) and energy efficiency (EE) in communications. Various definitions of EE will also be covered briefly. One typically used EE, i.e., bits-per-Joule, is theoretically derived and the ideal SE-EE tradeoff is introduced. It is recognized that a power amplifier (PA) is one of the most critical components in wireless communication systems and consumes a significant fraction of the total energy. The fundamental properties of PA, such as linearity and efficiency, are introduced. With the practical characteristics of PA, the detrimental effects of the signal non-linearity and power inefficiency of the PA on the SE, EE, and their tradeoff, are quantified.
Next, various existing EE-improving techniques are categorized from three perspectives: PA design, signal design and network design. This broad understanding based on the three categories will help motivate holistic design approaches to mitigate the non-ideal effects in real-life PA devices, and accelerate cross-domain research to further enhance the available techniques for high EE or good SE-EE tradeoff.
Last, the remaining challenges and future work for EE issue will be discussed.
Performance Analysis of Antenna Selection Techniques in MIMO-OFDM System with...IJECEIAES
In this paper we propose a new MIMO-OFDM model in which we consider various antenna selection techniques like Bulk selection and Per-subcarrier selection etc. with hardware impairments such as non-linearties of amplifiers, quantization noise, phase noise and I-Q imbalance etc. As we know that the transceiver hardware impairments limit the channel capacity and the energy efficiency of MIMO-OFDM system, so we can not neglect the fundamental impacts of these hardware impairments {Kappa (0.05 0.1)} on the energy efficiency in the high SNR domain. Therefore we analyze the Energy Efficiency of Bulk and Per-subcarrier antenna selection techniques with or without hardware impairments. It has been observed that the energy efficiency decreases as the value of these hardware impairments increases. As we compared the Bulk antenna selection with the Per-subcarrier antenna selection scheme, the Per-subcarrier antenna selection requires more number of RF (radio frequency) chains and transmits power in comparison to the Bulk selection. Due to this, the Bulk antenna selection technique is more energy efficient than Per-subcarrier antenna selection.
NETWORK ENERGY SAVING TECHNOLOGIES FOR GREEN WIRELESS ACCESS NETWORKSMadhav Thekkedath
Now a day smart phone users are increased and the energy consumption problem in mobile industry has become crucial. For the growth of mobile industry energy efficiency of the system must be improved. In order to improve the methods of network energy usage usually reduction of the Base Stations are to be employed.
The mobile industry faces a critical energy consumption challenge. By 2014 smart phones will exceed 1.82 billion units and surpass PCs as the most common web access devices. That’s why it is one of the important things. As all know that a smart is mainly for easy access to the network at anytime and anywhere. So it employs powering up all the Radio Base Stations (RBS’s) at maximum power. Since there are more number of service providers competition exist at the field of telecommunication. So it yields in more energy consumption to get monopoly among them. In the field of engineering, a system is usually designed to transform energy to useful work. Energy Efficiency can therefore be defined as the ratio of useful work to the total supplied energy. The useful work in a communication system refers to the effort to deliver modulated signals for information exchange. The definition of Energy Efficiency varies according to measured objects. There are two basic methods to measure Energy Efficiency. One way is to define Energy Efficiency as the ratio of efficient output power/energy to total input power/energy. This definition is widely used by systems and components such as power supply, Power Amplifiers, and antennas. The other way defines Energy Efficiency as the performance per unit of energy consumption.
The seminar gives a brief description about the methods to manage the network energy even when the on time access to network by mobile phones. Also gives a summary about existing and latest methodologies about the management in RBS’s.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Influence choice of the injection nodes of energy source on on-line losses of...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Each month, join us as we highlight and discuss hot topics ranging from the future of higher education to wearable technology, best productivity hacks and secrets to hiring top talent. Upload your SlideShares, and share your expertise with the world!
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A NEW DATA ENCODER AND DECODER SCHEME FOR NETWORK ON CHIPEditor IJMTER
System-on-chip (soc) based system has so many disadvantages in power-dissipation as
well as clock rate while the data transfer from one system to another system in on-chip. At the same
time, a higher operated system does not support the lower operated bus network for data transfer.
However an alternative scheme is proposed for high speed data transfer. But this scheme is limited to
SOCs. Unlike soc, network-on-chip (NOC) has so many advantages for data transfer. It has a special
feature to transfer the data in on-chip named as transitional encoder. Its operation is based on input
transitions. At the same time it supports systems which are higher operated frequencies. In this
project, a low-power encoding scheme is proposed. The proposed system yields lower dynamic
power dissipation due to the reduction of switching activity and coupling switching activity when
compared to existing system. Even-though many factors which is based on power dissipation, the
dynamic power dissipation is only considerable for reasonable advantage. The proposed system is
synthesized using quartus II 9.1 software. Besides, the proposed system will be extended up to
interlink PE communication with help of routers and PE’s which are performed by various
operations. To implement this system in real NOC’s contains the proposed encoders and decoders for
data transfer with regular traffic scenarios should be considered.
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICESijmnct
This paper investigates the application of advanced forward error correction techniques mainly: lowdensity parity checks (LDPC) code and polar code for IoT networks. These codes are under consideration
for 5G systems. Different code parameters such as code rate and a number of decoding iterations are used
to show their effect on the performance of the network. LDPC is performed better than polar code, over the
IoT network scenario considered in the work, for the same coding rate and the number of decoding
iterations. Considering bit error rate (BER) performance, LDPC with rate1/3 provided an improvement of
up to 2.6 dB for additive white Gaussian noise (AWGN) channel, and 2 dB for SUI-3 (frequency selective
fading channel model). LDPC code gives an improvement in throughput of about 12% as compared to
polar code with a coding rate of 2/3 over AWGN channel. The corresponding values over SUI-3 channel
are about 10%. Finally, in comparison with LDPC, polar code shows better energy saving for large
number of decoding iterations and high coding rates.
CODING SCHEMES FOR ENERGY CONSTRAINED IOT DEVICESijmnct_journal
This paper investigates the application of advanced forward error correction techniques mainly: lowdensity parity checks (LDPC) code and polar code for IoT networks. These codes are under consideration for 5G systems. Different code parameters such as code rate and a number of decoding iterations are used
to show their effect on the performance of the network. LDPC is performed better than polar code, over the IoT network scenario considered in the work, for the same coding rate and the number of decoding iterations. Considering bit error rate (BER) performance, LDPC with rate1/3 provided an improvement of
up to 2.6 dB for additive white Gaussian noise (AWGN) channel, and 2 dB for SUI-3 (frequency selective fading channel model). LDPC code gives an improvement in throughput of about 12% as compared to polar code with a coding rate of 2/3 over AWGN channel. The corresponding values over SUI-3 channel
are about 10%. Finally, in comparison with LDPC, polar code shows better energy saving for large number of decoding iterations and high coding rates.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Multistage interconnection networks a transition to opticaleSAT Journals
Abstract Several types of interconnection networks have been proposed for parallel processing. One of them is MIN’s (Multistage interconnection networks). With the better way of communication between processor and memory, MIN’s are less complex, good fault tolerance, high reliability, fast in communication and low cost. To achieving reliable, fast and flexible communication optical communication are necessary. Through optical networks we can achieve high performance and low latency also. With their great advantages over electronic networks, OMIN’s also having their own demands and problems. This paper focuses on the qualities of Optical MINs. Here we compare Optical MINs with electronic MIN’s with their design issues and gives some solution approaches for optical MINs to solve crosstalk.
Index Terms— Multistage Interconnection Networks (MIN), Optical networks, Crosstalk, Window methods.
Detection and Monitoring Intra/Inter Crosstalk in Optical Network on Chip IJECEIAES
Multiprocessor system-on-chip (MPSoC) has become an attractive solution for improving the performance of single chip in objective to satisfy the performance growing exponentially of the computer applications as multimedia applications. However, the communication between the different processors’ cores presents the first challenge front the high performance of MPSoC. Besides, Network on Chip (NoC) is among the most prominent solution for handling the on-chip communication. Besides, NoC potential limited by physical limitation, power consumption, latency and bandwidth in the both case: increasing data exchange or scalability of Multicores. Optical communication offers a wider bandwidth and lower power consumption, based on, a new technology named Optical Network-on-Chip (ONoC) has been introduced in MPSoC. However, ONoC components induce the crosstalk noise in the network on both forms intra/inter crosstalk. This serious problem deteriorates the quality of signals and degrades network performance. As a result, detection and monitoring the impairments becoming a challenge to keep the performance in the ONoC. In this article, we propose a new system to detect and monitor the crosstalk noise in ONoC. Particularly, we present an analytic model of intra/inter crosstalk at the optical devices. Then, we evaluate these impairments in objective to present the motivation to detect and monitor crosstalk in ONoC, in which our system has the capability to detect, to localize, and to monitor the crosstalk noise in the whole network. This system offers high reliability, scalability and efficiency with time running time less than 20 ms.
Performance Analysis of Bus Topology in Fiber Optic Communicationijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Design and Fiber Installation for University Campus Systemijtsrd
The health of a network depends on the quality of proper installation of the network infrastructure. The main aim of this research is to understand the basic aspects of a modern telecommunication network structure. Signal degradation dominants the performance of the fiber installation. By using MATLAB software, simulation results for signal degradation such as attenuation, dispersion and nonlinear effects are discussed. OTRD Optical Time Domain Reflectometer is a valuable tool for field engineers and service providers to monitor and detect the faults between access network and CO Central Office in real world analysis. In this paper, an overview related to the operation and function of a PON Passive Optical Network and required components to implement the fiber communication system are also described. Naing Naing Kyaw "Design and Fiber Installation for University Campus System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26812.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26812/design-and-fiber-installation-for-university-campus-system/naing-naing-kyaw
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. Energy Consumption and Energy Density in
Optical and Electronic Signal Processing
Rodney S. Tucker, Fellow, IEEE, and Kerry Hinton
Institute for a Broadband-Enabled Society (IBES) and Centre for Energy-Efficient
Telecommunications (CEET), Department of Electrical and Electronic Engineering,
University of Melbourne, Melbourne, Vic. 3010, Australia
DOI: 10.1109/JPHOT.2011.2166254
1943-0655/$26.00 Ó2011 IEEE
Manuscript received July 19, 2011; revised August 16, 2011; accepted August 21, 2011. Date of
publication August 30, 2011; date of current version September 23, 2011. Corresponding author:
R. Tucker (e-mail: r.tucker@unimelb.edu.au).
Abstract: We compare the energy consumption of digital optical and digital electronic signal
processing circuits, including the contributions to energy consumption of the optical to
electrical (O/E) converters and electrical to optical (E/O) converters, and the demultiplexers
(DEMUXs) and multiplexers (MUXs) required for electronic circuits to process high-speed
optical signals. This paper focuses on three key practical considerations, namely, energy
consumption, energy density, and the complexity of processing. We show that optical signal
processing is potentially competitive with electronics in very high-speed circuits that provide
only limited processing, i.e., when only a small number of processing operations are per-
formed on each bit of data. However, in applications that require anything more than limited
processing, electronics provides better energy efficiency and occupies a smaller footprint. In
these applications, electronics is likely to remain the technology of choice. More attention
needs to be paid to energy consumption issues in the research and development of new
digital optical technologies.
Index Terms: Optical signal processing, electronic signal processing, energy consumption,
energy efficiency.
1. Introduction
Today’s society is heavily reliant on a sophisticated and ubiquitous cyber infrastructure underpinned
by advanced digital signal processing technologies. In communications networks, signal processing
functions range from computing-intensive processes, such as video coding and decoding, as well
as forward error correction (FEC), to relatively simple functions, such as signal waveform reshaping
and regeneration. The majority of this signal processing today is carried out by electronics, most of
it in the form of complementary metal–oxide–semiconductor (CMOS) integrated circuits (ICs).
How does optical signal processing fit into the picture, and what is the future potential for digital
optical signal processing (sometimes referred to as all-optical signal processing) to replace some
electronic signal processing? Nonlinear optical components that can be used for signal processing
include semiconductor optical amplifiers (SOAs) [1], periodically polled Lithium Niobate (PPLN) [2],
highly nonlinear fibers (HNLFs) [3], and Silicon nanowires [4]. There have been many advances in
nonlinear digital optical signal processing over the past 30 years (see, for example [5]). However,
despite these advances, digital optical signal processing is not used in commercial telecommunica-
tions infrastructure.
There are several reasons why system designers prefer electronic signal processing over optical
signal processing: First, electronic signal processing provides outstanding performance and
Vol. 3, No. 5, October 2011 Page 821
IEEE Photonics Journal Energy Consumption and Energy Density
3. throughput (i.e., processing power and capacity for very high aggregated data rates). Second,
electronic devices are very small. The feature size in state-of-the-art CMOS transistors is on the
order of 22 nmValmost two orders of magnitude smaller than an optical wavelength in the com-
munications band and at least three orders of magnitude smaller than practical optical devices.
Third, electronic devices can be monolithically integrated with a very high device density. Conse-
quently, electronic devices are many orders of magnitude less expensive than active optical com-
ponents. Fourth, nearly all proposals for optical logic devices do not satisfy a variety of practical
criteria needed for optical logic functionality [6]. Finally, and perhaps most importantly, digital elec-
tronic devices consume a relatively small amount of energy.
The importance of low energy consumption cannot be over emphasized. Energy consumption
and the associated thermal issues have been a driving consideration behind much of the progress
made by the electronic IC industry over the past half century. In comparison, the optical signal
processing research community has paid scant attention to the question of energy consumption. A
key objective of the present paper is to highlight the importance of energy consumption as a
measure of performance in optical signal processing and to provide a benchmark for comparing
energy consumption in optical and electronic circuits. We argue that energy consumption issues are
critical in the development of new digital optical technologies.
In this paper, we compare signal processing in two domains: electronic and optical. If the signal to
be processed is in one domain and the processing is carried out in the other domain, then optical to
electrical (O/E) converters and electrical to optical (E/O) converters are required to convert the
signal between domains. These conversions add to the overall energy consumption and this energy
penalty can clearly be avoided if there is no change of domain. However, if the energy consumption
of signal processing in one domain is larger than in the other domain, it may be beneficial to change
domains.
A legitimate comparison between optical and electronic processing and switching must take into
account the performance limitations imposed and the energy consumed by ancillary components
such as the O/E and E/O converters needed to change domains [7], [8]. In addition, if the bit rate of
the data to be processed is high, demultiplexers (DEMUXs) and multiplexers (MUXs) may be
needed to reduce the bit rate to a level that the electronics can handle. A comparison of energy
consumption between technologies also requires that all sources of energy consumption are
considered, including energy consumed by interconnect wires in CMOS circuits, power-supply
energies, and losses in optical circuits.
The purpose of this paper is to compare the energy consumption of digital optical and digital
electronic signal processing circuits. We consider the device footprint and the energy density per
unit chip area and show how the energy density sets a limitation on the achievable density of
integration on a chip. In our comparison, we take into account the limitations imposed by the O/E
and E/O converters as well as MUXs and DEMUXs. We provide projections of device and circuit
performance out to the year 2020, based on published trends in device technologies such as the
International Technology Roadmap for Semiconductors (ITRS) [9].
We argue that optical signal processing may be competitive with electronics in simple high-speed
circuits with limited processing power. However, unless there are many orders of magnitude of
improvement in the energy efficiency of digital optical devices, electronics is likely to remain the
technology of choice for the vast majority of digital signal processing and switching applications.
2. Energy Model
In this section, we develop an energy model of optical and electronic signal processing circuits. The
model is based on an earlier model [8] but includes an important clock-frequency-dependent term in
the model of nonlinear optical devices that was not included in the earlier model. To ensure a
common basis for comparison between optical and electronic circuits, all of the signals processing
circuits considered here have optical inputs and optical outputs. This is a mode of operation that is
suited to optical signal processing and takes advantage of the ability of optical signal processing
circuits to operate directly on optical data in communications systems.
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 822
4. 2.1. Signal Processing Circuits
Fig. 1(a) shows an optical signal processing circuit, and Fig. 1(b) shows an electronic signal
processing circuit. Both circuits in Fig. 1 have m optical input ports and m optical output ports. The
data rate on each of the input ports in both circuits is B and the aggregate data rate across all input
ports is Baggregate ¼ m B. In both circuits, the signal processing function is carried out by an
interconnected array of digital devices [optical devices in Fig. 1(a) and electronic devices in
Fig. 1(b)], shown as rectangles in Fig. 1. These devices are basic signal processing elements such
as a logic gates or flip flops that form the building blocks of the overall circuit. In general, these
devices have multiple inputs and multiple outputs, as shown in Fig. 1. A fundamentally important
requirement on the characteristics of digital devicesVwhether optical or electronicVis that the
input and output signal levels satisfy the requirements of logic level restoration, cascadability, and
fan-out capabilities [6], [10], [11].
The chip area of both circuits (excluding O/E/O converters and (DE)MUXs) in Fig. 1 is A, and the
spacing between devices (device pitch) is d. Assuming a uniform distribution of devices across the
chip, the number of Ndevice devices on each chip is given by Ndevice ¼ A=d2
. The supply energy to
the signal processing chips in Fig. 1 per bit period of the input data (i.e., per 1=B s) is Esupply;O and
Esupply;E , and the energy consumed by each device per device operation is Edevice;O and Edevice;E for
the optical and electronic circuits, respectively. The supply powers Psupply;O and Psupply;E for the
optical and electronic circuits in Fig. 1(a) and (b) are simply the supply energies multiplied by the bit
rate, i.e., Psupply;O ¼ BEsupply;O, and Psupply;E ¼ BEsupply;E .
A key differentiating factor between the two circuits in Fig. 1 is that the electronic signal
processing circuit in Fig. 1(b) requires O/E conversion at the input ports and E/O conversion at the
output ports. Another difference is that the clock frequency (i.e., the processing frequency) fO of
optical circuit in Fig. 1(a) is equal to the bit rate B of the incoming data and the clock period O of the
optical circuit is therefore O ¼ 1=fO ¼ 1=B. In contrast, for electronic circuits, the input bit rate B
may be higher than the maximum achievable speed capability of the electronic devices. It is
therefore necessary to demultiplex the incoming data to a lower bit rate that matches the clock
frequency capabilities of the electronic devices. Similarly, if the output bit rate is larger than the
processing speed of the electronics, MUXs are required at the output ports. In Fig. 1(b), the
multiplexing and demultiplexing ratio is k. Therefore, clock frequency of the electronic circuit is
fE ¼ B=k and the clock period of the electronic circuit is E ¼ 1=fE ¼ k=B.
The total energy consumption per bit of all O/E and E/O converters in Fig. 1(b) is EO=E=O ¼
EO=E þ EE=O, where EO=E and EE=O are the total energies per bit in the O/E converters and E/O
converters, respectively. The total energy consumption of all MUXs and DEMUXs per input bit
period ð1=BÞ is EðDEÞMUX ¼ EDEMUX þ EMUX , where EDEMUX and EMUX are the total energy per bit of
the DEMUX and MUX, respectively.
Fig. 1. (a) Optical signal processing circuit. (b) Electronic signal processing circuit.
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 823
5. It is important to point out that while the clock frequency of the electronic signal processing chip in
Fig. 1(b) is reduced from the line rate B to B=k, the O/E and E/O converters and DEMUX and MUX
circuits must operate at B. The speed capabilities of these components ultimately limit the
achievable input bit rate B in Fig. 1(b). One approach to increasing the effective B for electronic
signal processing would be to use optical time division demultiplexing and multiplexing [12] at the
optical input and output ports in Fig. 1(b). Another approach would be to replace the m optical inputs
and outputs with mk inputs and outputs, each at a bit rate of B=k [6]. Both of these approaches are
beyond the scope of this paper.
2.2. Signal Processing Devices
In this section, we develop simple models for the optical devices in Fig. 1(a) and the electronic
devices in Fig. 1(b). As explained earlier, the term Bdevice[ in this paper refers to a building-block
signal processing element such as a logic gate. A building-block device typically comprises a
number of basic circuit elements. For example, Fig. 2(a) shows the circuit of a CMOS logic-level
inverter connected to a wire that connects this device to the input of another device or devices in the
circuit. This building-block device operates with well-defined input and output voltage levels and
provides the necessary logic level restoration and fan-out that enables it to serve as a building block
in a variety of signal processing circuits.
Fig. 2(b) is an example of a digital optical signal processing device. This particular example of a
digital optical device provides switching by new frequency generation. The new frequency
generation could be by 3-wave mixing in PPLN or 4-wave mixing in HNLF. In Fig. 2(b), the two
inputs are boosted to relatively high power levels using optical amplifiers (OAs) and are used to
generate a new (low-power) output. The unwanted frequencies are filtered out by Bragg grating
(BG). To provide logic level restoration and a fan-out of 2, the gains of the OAs are set so that the
net gain between inputs 1 and 2 and the output is 3 dB. For larger fan-out, the gain will need to be
higher. This may require significant power output from the OAs, depending upon the efficiency of
the process that generates the new frequency.
As shown in both Fig. 2(a) and (b), building-block devices for digital circuitsVboth electronic and
opticalVgenerally require a number of active devices (e.g., transistors, amplifiers, and nonlinear
components). Fig. 3 is a general schematic of a nonlinear device. In Fig. 3, the device is shown with
two inputs and two outputs, but more or fewer inputs and/or outputs are possible. The key
component of the device in Fig. 3 is one or more nonlinear elements that carry out the signal
processing operation. Also included are OAs at the inputs and/or outputs for signal-level restoration
and fan-out.
A key parameter in the analysis of digital signal processing devices is the quantum of energy
consumed by a device for each digital logic operation performed by that device. This quantum of
Fig. 2. (a) CMOS inverter circuit. (b) Schematic of a digital optical signal processing device
providing switching by new frequency generation. PPLN ¼ periodically polled Lithium Niobate.
HNLF ¼ highly nonlinear. OA ¼ optical amplifier. BG ¼ Bragg grating.
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 824
6. energy is Edevice, as defined earlier. As shown in Fig. 3, this energy includes the supply energy to all
of the ancillary active components in the device. The total signal input energy per operation on all
inputs to the device in Fig. 2 is Ein, and the total signal output energy on all outputs is Eout . This
output energy includes the energy consumed by the interconnects between devices. In general, the
interconnect energy is small in optical devices because the losses in optical waveguides are small.
However, interconnect energy generally dominates in high-speed electronic circuits [13].
A common problem in the optics literature is that the signal input energy of an optical device is
often confused with the Bswitching energy[ of the device. For example, some authors use the term
switching energy to refer to energy per bit at Input 1 or Input 2 in Fig. 2(b) or at the input to the HNLF
or PPLN. Because Ein ( Edevice [11], this typically leads to a gross underestimation of the energy
consumption of the device and provides a misleading picture of the device energy consumption.
Unfortunately, many papers do not provide sufficient information for the reader to estimate the total
energy consumption, i.e., Edevice in Fig. 3.
2.3. Device Energy Models
In this section, we present the device energy models used in our analysis. The CMOS device
energy Edevice;E per logic operation is modeled as follows:
Edevice;E ¼ EED
d
dref
þ EES
fref
fE
: (1)
The first term on the right-hand side of (1) is the dynamic energy of the device. It represents the
energy consumed in charging and discharging the capacitance of the transistors and the inter-
connect wires. In general, the capacitance of the interconnect wires dominates over the capaci-
tance of the transistors. Therefore, the dynamic energy supplied to each device per operation is
proportional to the length of the interconnect wires LW [see Fig. 2(a)]. For a given circuit, this wire
length scales with the device pitch d. In (1), EED is the device dynamic energy for d ¼ dref , and dref
is a reference device pitch.
The second term in (1) is the static energy of the device. It represents the energy associated with
device leakage currents. This energy is inversely proportional to the effective clock frequency
fE of
the device, where fE is the clock frequency of the electronic circuit, and
is the activity factor. The
average activity factor is the average number of digital operations that a device performs in each
clock period. In (1), EES is the device dynamic energy for
fE ¼ fref and fref is a reference clock
frequency. The static energy consumption of CMOS devices is becoming increasingly important as
CMOS devices scale to small sizes [14]. However, in the analysis presented here, we consider
circuits with effective clock frequencies
fE of 1 GHz and higher, where static energy consumption
is relatively small [14]. Therefore, we consider only the first term on the right-hand side of (1).
An important difference between digital CMOS devices and digital optical devices is that the
dynamic energy per logic operation in a CMOS device is determined by the charging and
Fig. 3. Optical or electronic signal processing device with two input and two outputs. The device
includes input and output amplifiers or buffers, and a nonlinear element.
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 825
7. discharging of a capacitance and is therefore independent of the clock frequency, but in digital
optical devices, the energy per logic operation is determined by the (continuous) optical power
required to activate an optical nonlinearity and the time taken to perform the logic operation.
Therefore, in all-optical digital devices, in which all data and device control are carried out by optical
signals, the nonlinear elements, amplifiers, and other active components require continuous supply
power, regardless of whether that device is performing a digital logic operation or not. In addition,
because Ein ( Edevice, the power consumption of nonlinear optical devices is approximately
constant. This is unavoidable because of the need to provide ancillary active devices required for
level restoration. Consequently, there is inverse relationship between the energy consumption per
bit in optical devices and the clock frequency. We therefore model the device energy per clock
period Edevice;O for digital optical devices as follows:
Edevice;O ¼ EO
fref
fO
(2)
where EO is the device energy when the effective clock frequency
fO is equal to a reference
clock frequency fref . Because interconnect losses are small in optical circuits, EO is independent
of the device pitch d.
2.4. Typical Device Energy Data
The calculations in this paper use energy data for typical optical and electronic devices. Fig. 4
shows some typical device energies as a function of time. The upper three curves (broken lines) in
Fig. 4 provide estimates of EO, EðDEÞMUX , and EO=E=O. Some of the data for EO in Fig. 4 were
obtained from a recent study [11] of optical signal processing. In [11], Edevice;O was calculated for a
variety of optical devices, using published experimental data, and (where possible) taking into
account all the peripheral components, including OAs, optical pump circuits and drivers, etc. (see
Fig. 3). In Fig. 4, we have included new data from some recent publications. As pointed out earlier,
many papers do not provide details of all contribution to energy consumption. For example, while
most papers document the signal levels at the inputs and outputs of the nonlinear element, many
give no information on the supply energy for the input and/or output OAs in Fig. 3. In papers where
energy data were missing, we made estimates of the total energy assuming OAs with power
conversion efficiencies of 50%. Arguably, a conversion efficiency of 50% is somewhat optimistic,
and therefore, the data should be viewed as being a lower limit on achievable energy.
Fig. 4. Device energies per bit against time. The optical device energy per bit EO is normalized to a
reference clock frequency of fref ¼ 100 GHz. HNLF ¼ highly nonlinear fiber, SOA ¼ semiconductor
optical amplifier, PPLN ¼ periodically polled Lithium Niobate.
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 826
8. The data presented in [11] are for Edevice;O and, therefore, do not take account of the clock
frequency, which varies widely between different experiments published in the literature. To
overcome this, we have renormalized the data presented in [11] from Edevice;O to EO, using (2) with a
reference clock frequency of fref ¼ 100 GHz. This renormalization removes uncertainty about the
influence of different clock frequencies. The broken line is a line of best fit to these data. The MUX
and DEMUX data are taken from a survey of device capabilities presented in [15]. Data points are
shown in Fig. 4 for an InP heterojunction bipolar transistor (HBT) DEMUX and a projection for future
Silicon–Germanium (SiGe) high-speed MUX and DEMUX circuits [15]. The O/E/O data in Fig. 4 are
based on estimates for long-reach WDM transmitters and receivers. Thus, these data are relevant
to signal processing circuits used within long-reach transmission systems.
Some nonlinear devices reported in the literature use relatively long active regions in order to
maximize the interaction between the optical signal field and the nonlinear medium. For example,
recent experiments based on HNLF have used lengths of fiber as large as 1 km. Unfortunately,
devices larger than a few centimeters or, at most, a few tens of centimeters, will have too large a
footprint for practical applications and are also likely to suffer from difficulties with clock skew [13].
Therefore, the data in Fig. 4 do not include any devices that are larger than a few tens of
centimeters.
The device switching energy of a simple CMOS inverter is shown in the lower part of Fig. 4.
Decreasing device feature sizes as a function of time are shown. The smallest commercial device at
the time of writing is 22 nm, but projected energies out to feature sizes of 11 nm are included in
Fig. 4, based on data in the ITRS [9].
The lower curve in the bottom part of Fig. 4 is the energy consumed by a CMOS inverter single
gate, excluding the interconnect wires. The upper curve in the bottom part of Fig. 4 is an estimate
of the total energy, including the energy consumed by the interconnect wires in a typical CMOS
IC [16]. To estimate this total energy including the interconnect wires, we considered a commercial
32-nm two-core processor and estimated that the average total energy per transistor (including
interconnect wires) per transition is around 3 fJ [16]. From this, we estimate that the average length
of and interconnect wire length in a 32-nm processor is LW $ 3 m. The data in Fig. 4 for other
feature sizes were obtained by scaling the average interconnect wire length to the transistor
feature size and using other parameters from the ITRS [9]. The rate of improvement of total CMOS
energy including wires in Fig. 4 is approximately 25% per annum or a factor-of-10 improvement
over 10 years. Note that the rate of improvement of total CMOS energy including wires is less
rapid than for an isolated CMOS gate.
The total CMOS energy Edevice;E including the interconnect wires is more than two orders of
magnitude larger than the energy of an isolated CMOS gate, indicating that the consumption of
each device is dominated by the energy in the interconnect wires. This difference in energies is an
upper limit as it applies to a fairly complex processor. In simpler circuits, the average interconnect
lengths may be shorter, and this energy difference may be smaller. Our analysis of the circuit in
Fig. 1(b) uses the Edevice;E in Fig. 4. From Fig. 4, Edevice;E (including interconnect wires) is 3 fJ in
2010 and 0.3 fJ in 2020. This translates into the EE and dref figures given in Table 1.
3. Analysis
In this section, we compare the energy consumption of the circuits in Fig. 1 using estimated device
energies for the years 2010 and 2020. Fig. 5 and Table 1 summarize the data used in the analysis.
The optical device energy EO (using fref ¼ 100 GHz) is 1 pJ in 2010 and 200 fJ in 2020. This
assumes significant improvements in optical device technology over the next 10 years.
As pointed out earlier, we have ignored leakage current (i.e., static energy consumption) in
CMOS devices. Therefore, EES in Table 1 is set to zero. This is justified by analyses of CMOS
devices including leakage [14] that confirm that at high clock frequencies, the static energy
consumption in CMOS is smaller than the dynamic energy consumption.
The data for Edevice;E , EðDEÞMUX , and EO=E=O in Fig. 5 and Table 1 are taken directly from Fig. 4.
Note that the O/E/O energies in Fig. 5 and Table 1 apply to long-reach optical transmitters and
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 827
9. receivers. These energies could be as much as an order of magnitude smaller if the transmitters
and receivers are optimized for transmission over short distances [17].
3.1. Complexity of Signal Processing
One of the parameters in our analysis of energy consumption is the complexity of the signal
processing carried out by the chip. We characterize the complexity of the signal processing in terms
of the number Nop of digital operations performed on each bit of input data. The number of
operations per bit of input data can vary widely, depending on the function being carried out by the
circuit. For example, in an optical wavelength converter based on cross-phase modulation in SOAs
[18], each incoming bit undergoes very little signal processing, and Nop would typically be on the
order of one or two. For more complicated operations such as Internet protocol (IP) packet header
recognition, more digital operations are needed, and Nop would be on the order of 100 or even
larger for realistic size networks. In sophisticated signal processing circuits such as FEC [19], Nop
would be more than 103
or 104
.
In the optical circuit in Fig. 1(a), the clock period is clock;O ¼ 1=B, and the number of operations
per input bit achievable on a chip containing Ndevice;O devices, each of which is capable of one
operation per clock period clock;O, is given by
Nop ¼
Ndevice;O=m (3)
where
is the activity factor. Similarly, in the electronic circuit in Fig. 1(b), the clock period is
clock;E ¼ k=B, and the number of operations per input bit on a chip containing Ndevice;E devices,
TABLE 1
Device energies
Fig. 5. Data points used in analysis, marked as BX[.
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 828
10. each of which is capable of one operation per clock period clock;E , is given by
Nop ¼
Ndevice;E =mk: (4)
3.2. Total Energy per Input Bit Processed
The total energy Ebit;O per input bit consumed by the optical chip in Fig. 1(a) per input bit period or
optical clock period ð1=BÞ is equal to the number of logic operations Nop per input but multiplied by
the total energy Edevice;O per device per logic operation. Using (2), this gives
Ebit;O ¼ NopEdevice;O ¼ NopEO
fref
fO
: (5)
Similarly, for the electronic chip in the circuit in Fig. 1(b), the total energy consumed by the chip
per input bit is Ebit;E ¼ Nop Edevice;E . Using (1) with EES ¼ 0 and adding the energy consumed by the
O/E/O converters and (DE)MUXs, the total energy per bit in the circuit in Fig. 1(b) is
Ebit;E ¼ NopEED
d
dref
þ EO=E=O þ EðDEÞMUX : (6)
Fig. 6 shows the total energy per bit Ebit;E and Ebit;O against the number of operations Nop per bit
for optical and electronic circuits using the data in Table 1. The energy per bit in an optical circuit
increases linearly with the number of digital operations per bit and decreases as the effective clock
frequency
fO increases and EO decreases. In contrast, the energy per bit in an electronic circuit is
largely independent of the number of operations per bit and is largely independent of the clock
frequency. However, it is important to remember that the effective clock frequency of an electronic
circuit is limited to around 100 GHz by the limited speed capabilities of the O/E/O converters and the
(DE)MUXs. The energy per bit in the electronic circuit is dominated by the (DE)MUXs and, to a
smaller extent, the O/E/O converters. The energy per bit in electronic circuits for 2010 includes both
the O/E/O converters and the (DE)MUXs, but for 2020, we have drawn two curvesVone with both
the O/E/O converters and the (DE)MUXs and one with O/E/O converters only to allow for situations
where the bit rate is low enough not to require (DE)MUXs. Fig. 6 shows that for circuits with low
complexity (fewer than 10 operations per bit), optical and electronic circuits consume similar
amounts of energy. However, for more than 100 operations per bit, electronic circuits are generally
Fig. 6. Total energy per bit processed against number of operations per bit. The solid diagonal lines give
Ebit;O for optical devices with
fO ¼ 100 GHz and 1 THz, as well as with EO ¼ 1 pJ and 0.2 pJ. The
upper two broken lines give Ebit;E in 2010 and 2020 for electronic circuits with DE(MUX) and O/E/O
circuits at the inputs and outputs as shown in Fig. 1(b). The lowest broken curve gives Ebit;E in 2020 for
circuits with O/E/O converters only (i.e., without DE(MUX)s). This would apply in situations where the bit
rate is low enough not to require the (DE)MUXs.
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 829
11. more competitive from an energy consumption point of view. However, as stated earlier, optical
circuits can, in principle, operate at higher speed.
3.3. Chip Power Density
In this section, we explore how the chip power density affects scaling properties of optical chips
and compare the scaling properties of optical and electronic chips. The power density (i.e., the
supply power per unit chip area) is a key parameter that limits the integration density and the clock
speed in electronic ICs. In today’s IC chips, thermal considerations limit the maximum allowable
power density to less than about 100 W/cm2
[9], [20]. The implications of power management on the
scaling properties of electronic chips are reasonably well understood [20], but this topic has
received little attention in the optics literature.
If the output signal energy from an electronic chip is small compared with the supply energy, all of
the supply energy is converted to heat. In an optical circuit, some of the supply energy is converted
to heat. However, in principle, some unused optical energy (e.g., unused spontaneous emission or
unused nonlinear mixing products) could be removed from the chip, either using waveguides or by
free space propagation, and dissipated off-chip. In the present analysis, we consider two power
density limits for optical circuits: 100 W/cm2
Vthe same limit as for electronic circuits, and 1 kW/cm2
.
This latter limit would apply for a circuit where 90% of the supply energy is dissipated off-chip in
optical form.
In [11], it is shown that the input and output power to and from the chip at the signal input and
output ports is small compared with the supply power. Therefore, these powers are ignored in the
present analysis. In addition, we do not include the O/E and E/O converters and the (DE)MUXs in
this part of the analysis.
The chip supply power for optical and electronic circuits (see Fig. 1) is given by
Psupply;X ¼ Baggregate Ebit;X (7)
where, as before, Baggregate ¼ mB. The chip power density Pd for optical and the electronic chips is
given by [13]
Pd;X ¼
Psupply;X
A
¼
mBEbit;X
d2Ndevice
¼
fX Edevice;X
d2
(8)
where Psupply;X ¼ B Esupply;X , and the term X in the subscripts in (7) and (8) can be either O or E.
From (8), the device energy for both optical and electronic devices can be written as
Edevice;X ¼ Pd;X
d2
fX
: (9)
Fig. 7 shows Edevice;X from (9) plotted (broken lines) against the device pitch d, for
Pd ¼ 100 W/cm2
and for
fX ¼ 1 GHz; 100 GHz; and 10 THz. These broken lines in Fig. 7 give
the upper bound on allowable device energy for a given
fX and d. If the device energy for a
particular device falls above one of these lines, then the chip power density will exceed 100 W/cm2
.
Shown on the upper horizontal axis is the power consumed by each device, namely d2
Pd ¼
fx Edevice;X . Also plotted in Fig. 7 is Edevice for an 11-nm CMOS device (i.e., Edevice;E ) from (1). At a
device pitch of 10À7
m, a chip using these devices would be limited to an effective clock frequency
fX of less than 1 GHz. However, at larger device pitches, the effective clock frequency
fX can, in
principle, increase to around 100 GHz, with a the effective clock frequency limited by the O/E/O
converters and the (DE)MUXs.
For comparison with the 11-nm CMOS example, Fig. 7 shows vertical lines representing the
constant power per device of optical devices with EO ¼ 200 fJ and 1 pJ. This indicates that the
device pitch of optical devices is limited by thermal considerations to a minimum around 10À4
m.
However, with a device pitch on this order, optical devices are capable, in principle, of operating at
less than 100 W/cm2
of dissipated power up to 10 THz and beyond.
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 830
12. 3.4. Device Integration Density
As shown in Section 3.3 above, the number of devices that can be integrated on a chip is limited
by dissipation on the chip. Fig. 8 provides an indication of historical trends in chip integration
densities and relates this to energy limits for 11-nm CMOS and optical devices. The upper diagonal
line traces improvements in the density of integration over time for CMOS ICs. For CMOS devices,
the number of devices on a 1-cm2
chip has grown at a rate of around a factor of two every 18 months,
according to Moore’s law [21]. There is no equivalent law for optical ICs; therefore, we have given the
lower diagonal line in Fig. 8 the same slope as the CMOS curve. Trends to date indicate that it is
unlikely optical ICs will match Moore’s law, and we have therefore adopted this as a rather optimistic
upper limit on the rate of improvement. This curve passes through a data point at an integration
density of $100 cmÀ2
in 2005 [22]. Note that the circuits described in [22] are not specifically
designed for digital signal processing, but [22] gives an indication of the practical level of optical
device integration in the 2005 timeframe.
The upper two horizontal lines in Fig. 8 represent the dissipation-limited number of 11-nm CMOS
devices per 1-cm2
of chip area for circuits with effective clock frequencies of 1 GHz and 100 GHz.
CMOS chips, with around 2 billion devices per 1-cm2
of chip area, operate close to the thermal
dissipation limit at 1 GHz. Dissipation limits for 11-nm CMOS restrict the device density to about
1.5 million per cm2
if the clock frequency is increased to 100 GHz. The lower four horizontal lines
Fig. 8. Number of devices per 1-cm2
chip.
Fig. 7. Supply energy per bit against device pitch.
IEEE Photonics Journal Energy Consumption and Energy Density
Vol. 3, No. 5, October 2011 Page 831
13. in Fig. 8 represent the dissipation-limited number of optical devices for the two optical device
energies EO used in Fig. 7 and for power density limits of 100 W/cm2
and 1 kW/cm2
. While we
recognize that there is no photonic equivalent to Moore’s law, Fig. 8 suggests that there is some
potential for future increases in the integration density of photonic ICs. However, it appears likely
that photonic integration densities will be restricted by thermal limits to less than 104
cmÀ2
, i.e.,
around six orders of magnitude lower than the integration density of CMOS circuits.
4. Conclusion
Energy consumption is a key consideration in the development components, devices, and circuits
for digital optical signal processing. We have shown that, from an energy consumption point of view,
digital optical signal processing is potentially competitive with electronic signal processing if the
signal to be processed is in optical form and if the signal processing function is simpleVi.e., when
there are only a few digital operations performed on each bit of data. For circuits requiring more
operations on each bit of data, electronic signal processing uses less energy, even if the data to be
processed is in optical form.
It is often argued that optical signal processing will replace digital signal processing because of its
high-speed capabilities. However, simplistic arguments based on speed alone often miss the
critically important point that digital optical devices are generally very energy hungry. Unless there
are many-orders-of-magnitude improvement in the energy efficiency of digital optical devices,
electronics is likely to remain the technology of choice for the vast majority of digital signal pro-
cessing and switching applications in telecommunications networks.
There is a large disparity between the research literature on CMOS signal processing and the
research literature on optical signal processing. While energy considerations are primary drivers in
CMOS RD, energy considerations receive very little attention in the optical signal processing
literature. Even in papers where energy is mentioned, full details are often missing, and incorrect
interpretations of energy consumption in optical devices are not uncommon. We believe that energy
consumption is a potentially significant barrier to the commercial exploitation of digital optical cir-
cuits. More attention needs to be paid to energy consumption issues in the research and devel-
opment of new digital optical technologies.
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