This document presents a VHDL implementation of an IEEE 754 floating point unit using a carry look ahead adder and radix-4 modified Booth encoder multiplier. The floating point unit performs single precision floating point multiplication. It consists of blocks to calculate the sign bit, add the exponents, multiply the significands using the modified Booth encoding technique, normalize the result, detect overflow/underflow, and implement pipelining. VHDL simulation results show that this floating point multiplier design has lower delay and power consumption compared to an array multiplier implementation.
A Novel Efficient VLSI Architecture for IEEE 754 Floating point multiplier us...IJERA Editor
Due to advancement of new technology in the field of VLSI and Embedded system, there is an increasing
demand of high speed and low power consumption processor. Speed of processor greatly depends on its
multiplier as well as adder performance. In spite of complexity involved in floating point arithmetic, its
implementation is increasing day by day. Due to which high speed adder architecture become important. Several
adder architecture designs have been developed to increase the efficiency of the adder. In this paper, we
introduce an architecture that performs high speed IEEE 754 floating point multiplier using modified carry
select adder (CSA). Modified CSA depend on booth encoder (BEC) Technique. Booth encoder, Mathematics is
an ancient Indian system of Mathematics. Here we are introduced two carry select based design. These designs
are implementation Xilinx Vertex device family.
Design and Implementation of 8 Bit Multiplier Using M.G.D.I. TechniqueIJMER
In this paper we have implemented Radix 8 High Speed Low Power Binary Multiplier using
Modified Gate Diffusion Input(M.G.D.I) technique. Here we have used “Urdhva-tiryakbhyam”(
Vertically and crosswise ) Algorithm because as compared to other multiplication algorithms it shows
less computation and less complexity since it reduces the total number of partial products to half of it.
This multiplier at gate level can be design using any technique such as CMOS, PTL and TG but design
with new MGDI technique gives far better result in terms of area, switching delay and power
dissipation. The radix 8 High Speed Low Power Pipelined Multiplier is designed with MGDI technique
in DSCH 3.5 and layout generated in Microwind tool. The Simulation is done using 0.12μm technology
at 1.2 v supply voltage and results are compared with conventional CMOS technique. Simulation result
shows great improvement in terms of area, switching delay and power dissipation.
DLD Presentation By Team Reboot,Rafin Rayan,EUBRafin Rayan
Digital Logic Design Presentation By Team Rboot ,Student's of Computer Science & Engineering Department , European University Of Bangladesh . Total 4 Member's Team &Team Leader is Rafin Rayan (Dept. Of CSE,EUB)
A Novel Efficient VLSI Architecture for IEEE 754 Floating point multiplier us...IJERA Editor
Due to advancement of new technology in the field of VLSI and Embedded system, there is an increasing
demand of high speed and low power consumption processor. Speed of processor greatly depends on its
multiplier as well as adder performance. In spite of complexity involved in floating point arithmetic, its
implementation is increasing day by day. Due to which high speed adder architecture become important. Several
adder architecture designs have been developed to increase the efficiency of the adder. In this paper, we
introduce an architecture that performs high speed IEEE 754 floating point multiplier using modified carry
select adder (CSA). Modified CSA depend on booth encoder (BEC) Technique. Booth encoder, Mathematics is
an ancient Indian system of Mathematics. Here we are introduced two carry select based design. These designs
are implementation Xilinx Vertex device family.
Design and Implementation of 8 Bit Multiplier Using M.G.D.I. TechniqueIJMER
In this paper we have implemented Radix 8 High Speed Low Power Binary Multiplier using
Modified Gate Diffusion Input(M.G.D.I) technique. Here we have used “Urdhva-tiryakbhyam”(
Vertically and crosswise ) Algorithm because as compared to other multiplication algorithms it shows
less computation and less complexity since it reduces the total number of partial products to half of it.
This multiplier at gate level can be design using any technique such as CMOS, PTL and TG but design
with new MGDI technique gives far better result in terms of area, switching delay and power
dissipation. The radix 8 High Speed Low Power Pipelined Multiplier is designed with MGDI technique
in DSCH 3.5 and layout generated in Microwind tool. The Simulation is done using 0.12μm technology
at 1.2 v supply voltage and results are compared with conventional CMOS technique. Simulation result
shows great improvement in terms of area, switching delay and power dissipation.
DLD Presentation By Team Reboot,Rafin Rayan,EUBRafin Rayan
Digital Logic Design Presentation By Team Rboot ,Student's of Computer Science & Engineering Department , European University Of Bangladesh . Total 4 Member's Team &Team Leader is Rafin Rayan (Dept. Of CSE,EUB)
SINGLE PRECISION FLOATING POINT MULTIPLIER USING SHIFT AND ADD ALGORITHMAM Publications
Floating-point numbers are widely adopted in many applications due to their dynamic representation
capabilities. Basically floating point numbers are one possible way of representing real numbers in binary format.
Floating-point representation is able to retain its resolution and accuracy compared to fixed-point representations.
Multiplying floating point numbers is also a critical requirement for DSP applications involving large dynamic range.
The IEEE has produced a standard to define floating point representation and arithmetic which is known as IEEE
754 standards and which is the most common representation today for real numbers on computer. The IEEE 754
standard presents two different floating point formats, Binary interchange format and Decimal interchange format.
This paper presents a single precision floating point multiplier based on shift and add algorithm that supports the
IEEE 754 binary interchange format..
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.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
RTL Verification and FPGA Implementation of 4x4 Vedic MultiplierMohd Esa
The objective of this paper is to study 4x4 Vedic multiplier.
Multiplication is an important fundamental function in arithmetic operations.
Vedic multiplier using Urdhva-Tiryagbyam sutra is predominant in
performance evaluation of parameters such as power, area & delay. This paper
presents design, verification and FPGA implementation of Vedic multiplier.
Verification is carried out in Questa Sim 10.4e using System Verilog HVL and
design is carried out in Xilinx ISE Design Suite 14.7 using Verilog HDL
environment
Implementation and Simulation of Ieee 754 Single-Precision Floating Point Mul...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Design of QSD Number System Addition using Delayed Addition TechniqueKumar Goud
Abstract: Quaternary number system is a base-4 numeral system. Using Quaternary Signed Digit (QSD) number system may also execute carry free addition, borrow free subtraction and multiplication. The QSD number system wants a different group of prime modulo based logic elements for each arithmetic operation. In this work we extend this QSD addition to Delayed addition in place of carry free addition. Carry free addition generates intermediate carry and intermediate sum, in this carry propagation is required to generate intermediate sum. To reduce carry propagation we evaluated delayed addition. This delayed addition reduces carry propagation and improves arithmetic calculations. We present both QSD and Floating –point single precision addition using delayed addition. The design work is carried by using Verilog HDL in ISE.
Keywords: QSD, DA, CFA and Floating-Point.
The impact of innovation on travel and tourism industries (World Travel Marke...Brian Solis
From the impact of Pokemon Go on Silicon Valley to artificial intelligence, futurist Brian Solis talks to Mathew Parsons of World Travel Market about the future of travel, tourism and hospitality.
We’re all trying to find that idea or spark that will turn a good project into a great project. Creativity plays a huge role in the outcome of our work. Harnessing the power of collaboration and open source, we can make great strides towards excellence. Not just for designers, this talk can be applicable to many different roles – even development. In this talk, Seasoned Creative Director Sara Cannon is going to share some secrets about creative methodology, collaboration, and the strong role that open source can play in our work.
SINGLE PRECISION FLOATING POINT MULTIPLIER USING SHIFT AND ADD ALGORITHMAM Publications
Floating-point numbers are widely adopted in many applications due to their dynamic representation
capabilities. Basically floating point numbers are one possible way of representing real numbers in binary format.
Floating-point representation is able to retain its resolution and accuracy compared to fixed-point representations.
Multiplying floating point numbers is also a critical requirement for DSP applications involving large dynamic range.
The IEEE has produced a standard to define floating point representation and arithmetic which is known as IEEE
754 standards and which is the most common representation today for real numbers on computer. The IEEE 754
standard presents two different floating point formats, Binary interchange format and Decimal interchange format.
This paper presents a single precision floating point multiplier based on shift and add algorithm that supports the
IEEE 754 binary interchange format..
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.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
RTL Verification and FPGA Implementation of 4x4 Vedic MultiplierMohd Esa
The objective of this paper is to study 4x4 Vedic multiplier.
Multiplication is an important fundamental function in arithmetic operations.
Vedic multiplier using Urdhva-Tiryagbyam sutra is predominant in
performance evaluation of parameters such as power, area & delay. This paper
presents design, verification and FPGA implementation of Vedic multiplier.
Verification is carried out in Questa Sim 10.4e using System Verilog HVL and
design is carried out in Xilinx ISE Design Suite 14.7 using Verilog HDL
environment
Implementation and Simulation of Ieee 754 Single-Precision Floating Point Mul...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Design of QSD Number System Addition using Delayed Addition TechniqueKumar Goud
Abstract: Quaternary number system is a base-4 numeral system. Using Quaternary Signed Digit (QSD) number system may also execute carry free addition, borrow free subtraction and multiplication. The QSD number system wants a different group of prime modulo based logic elements for each arithmetic operation. In this work we extend this QSD addition to Delayed addition in place of carry free addition. Carry free addition generates intermediate carry and intermediate sum, in this carry propagation is required to generate intermediate sum. To reduce carry propagation we evaluated delayed addition. This delayed addition reduces carry propagation and improves arithmetic calculations. We present both QSD and Floating –point single precision addition using delayed addition. The design work is carried by using Verilog HDL in ISE.
Keywords: QSD, DA, CFA and Floating-Point.
The impact of innovation on travel and tourism industries (World Travel Marke...Brian Solis
From the impact of Pokemon Go on Silicon Valley to artificial intelligence, futurist Brian Solis talks to Mathew Parsons of World Travel Market about the future of travel, tourism and hospitality.
We’re all trying to find that idea or spark that will turn a good project into a great project. Creativity plays a huge role in the outcome of our work. Harnessing the power of collaboration and open source, we can make great strides towards excellence. Not just for designers, this talk can be applicable to many different roles – even development. In this talk, Seasoned Creative Director Sara Cannon is going to share some secrets about creative methodology, collaboration, and the strong role that open source can play in our work.
Gave a talk at StartCon about the future of Growth. I touch on viral marketing / referral marketing, fake news and social media, and marketplaces. Finally, the slides go through future technology platforms and how things might evolve there.
The Six Highest Performing B2B Blog Post FormatsBarry Feldman
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Each technological age has been marked by a shift in how the industrial platform enables companies to rethink their business processes and create wealth. In the talk I argue that we are limiting our view of what this next industrial/digital age can offer because of how we read, measure and through that perceive the world (how we cherry pick data). Companies are locked in metrics and quantitative measures, data that can fit into a spreadsheet. And by that they see the digital transformation merely as an efficiency tool to the fossil fuel age. But we need to stretch further…
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DESIGN OF DOUBLE PRECISION FLOATING POINT MULTIPLICATION ALGORITHM WITH VECTO...jmicro
This paper presents floating point multiplier capable of supporting wide range of application domains like scientific computing and multimedia applications and also describes an
implementation of a floating point multiplier that supports the IEEE 754-2008 binary interchange format with methodology for estimating the power and speed has been developed. This Pipelined vectorized floating point multiplier supporting FP16, FP32, FP64 input data and reduces the area, power, latency and increases throughput. Precision can be implemented by taking the 128 bit input operands.The floating point units consumeless power and small part of total area. Graphic Processor Units (GPUS) are specially tuned for
performing a set of operations on large sets of data. This paper also presents the design of a Double precision floating point multiplication algorithm with vector support. The single precision floating point multiplier is having a path delay of 72ns and also having the operating frequency of 13.58MHz.Finally this implementation is done in Verilog HDL using Xilinx ISE-14.2.
Implementation of 32 Bit Binary Floating Point Adder Using IEEE 754 Single Pr...iosrjce
Field Programmable Gate Arrays (FPGA) are increasingly being used to design high- end
computationally intense microprocessors capable of handling both fixed and floating- point mathematical
operations. Addition is the most complex operation in a floating-point unit and offers major delay while taking
significant area. Over the years, the VLSI community has developed many floating-point adder algorithms
mainly aimed to reduce the overall latency. The Objective of this paper to implement the 32 bit binary floating
point adder with minimum time. Floating point numbers are used in various applications such as medical
imaging, radar, telecommunications Etc. Here pipelined architecture is used in order to increase the
performance and the design is achieved to increase the operating frequency. The logic is designed using VHDL.
This paper discusses in detail the best possible FPGA implementation will act as an important design resource.
The performance criterion is latency in all the cases. The algorithms are compared for overall latency, area,
and levels of logic and analyzed specifically for one of the latest FPGA architectures provided by Xilinx.
This paper presents different computational algorithms to implement single
precision floating point division on field programmable gate arrays (FPGA).
Fast division computation algorithms can apply to all division cases by
which an efficient result will be obtained in terms of delay time and power
consumption. 24-bit Vedic multiplication (Urdhva-Triyakbhyam-sutra)
technique enhances the computational speed of the mantissa module and this
module is used to design a 32-bit floating point multiplier which is the
crucial feature of this proposed design, which yields a higher computational
speed and reduced delay time. The proposed design of floating-point divider
using fast computational algorithms synthesized using Verilog hardware
description language has a 32-bit floating point multiplier module unit and a
32-bit floating point subtractor module unit. Xilinx Spartan 6 SP605
evaluation platform is used to verify this proposed design on FPGA.
Synthesis results provide the device utilization and propagation delay
parameters for the proposed design and a comparative study is done with
previous work. Input to the divider is provided in IEEE 754 32-bit formats.
A Fast Floating Point Double Precision Implementation on FpgaIJERA Editor
In the modern day digital systems, floating point units are an important component in many signal and image
processing applications. Many approaches of the floating point units have been proposed and compared with
their counterparts in recent years. IEEE 754 floating point standard allows two types of precision units for
floating point operations, single and double. In the proposed architecture double precision floating point unit is
used and basic arithmetic operations are performed. A parallel architecture is proposed along with the high
speed adder, which is shared among other operations and can perform operations independently as a separate
unit. To improve the area efficiency of the unit, carry select adder is designed with the novel resource sharing
technique which allows performing the operations with the minimum usage of the resources while computing
the carry and sum for „0‟ and „1‟. The design is implemented using the Xilinx Spartan 6 FPGA and the results
show the 23% improvement in the speed of the designed circuit
Design and Analysis of High Performance Floating Point Arithmetic Unitijtsrd
A floating point arithmetic unit designed to perform operations on floating point numbers as well as fixed point numbers. Floating point numbers can support a much wider range of values in comparison to fixed point representation. Floating Point units are mainly used in high speed objects recognition system, high performance computer systems, embedded systems and mobile applications. To represent very small values or very large values, large range is required as the integer representation is no longer appropriate to represent these numbers so these values can be represented by using floating point representation that is based on the IEEE 754 standard. The proposed floating point arithmetic unit is designed using single stage implementation. Due to single stage implementation the complex logic operations which consist of various multiple numbers of stages are converted into single stage implementation. So by using single stage implementation the time requires to reach data from input to output becomes less. The proposed unit is designed in VHDL, simulated in Questa Sim simulator and implemented on vertex 7 FPGA. Naresh Kumar | Onkar Singh | Harjit Singh "Design and Analysis of High Performance Floating Point Arithmetic Unit" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38049.pdf Paper URL : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/38049/design-and-analysis-of-high-performance-floating-point-arithmetic-unit/naresh-kumar
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Design and implementation of complex floating point processor using fpgaVLSICS Design
This paper presents complete processor hardware with three arithmetic units. The first arithmetic unit can
perform 32-bit integer arithmetic operations. The second unit can perform arithmetic operations such as
addition, subtraction, multiplication, division, and square root on 32-bit floating point numbers. The third
unit can perform arithmetic operations such as addition, subtraction, multiplication on complex numbers.
The specific advancement in this processor is the new architecture introduced for complex arithmetic unit.
In general complex floating point arithmetic hardware consists of floating to fixed and fixed to floating
conversions. But using such hardware will lead to compromise between accuracy and number of bits used
to represent the fixed point equivalent of floating point numbers. The proposed architecture avoids that
compromise and it is implemented with less number of look-up tables to save around 5500 logic gates. The
complex numbers are represented using a subset of IEEE754 standard floating point format, 16-bits for
real part and 16-bits for imaginary part. The floating point arithmetic unit works on 32-bit IEEE754 single
precision numbers. The instruction set is specially designed to support integer, floating point and complex
floating point arithmetic operations. The on-chip RAM is 8kBytes and is extendable up to 64kBytes. As the
processor is designed to implement on FPGA, the embedded block RAMs are utilized as RAM.
Design of 32-bit Floating Point Unit for Advanced ProcessorsIJERA Editor
Floating Point Unit is one of the integral unit in the Advanced Processors. The arithmetic operations on floating point unit are quite complicated. They are represented in IEEE 754 format in either 32-bit format (single precision) or 64-bit format (double precision). They are extensively used in high end processors for various applications such as mathematical analysis and formulation, signal processing etc. This paper describes the detailed process for the computation of addition, subtraction and multiplication operations on floating point numbers. It has been designed using VHDL. The design has been simulated and synthesized to identify the area occupied and its performance in terms of delay.
A High Speed Transposed Form FIR Filter Using Floating Point Dadda MultiplierIJRES Journal
There is a huge demand in high speed area efficient parallel FIR filter using floating point dadda algorithm, due to increase performance of processing units. Area and spped are usually confictiong constraints so that improving speed results mostly in large areas. In our research we will try to determine the best solution to this problem by comparing the results of different multipliers. Different sized of two algorithm for high speed hardware multipliers were studied and implemented ie.dadda and booth multipliers. The working of these two multipliers were studied and implementing each of them separately in VHDL. The results of this research will help us to choose the better option between multipliers for floating point multiplier for fabricating different system.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
At36276280
1. Subitha. M. B Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.276-280
RESEARCH ARTICLE
www.ijera.com
OPEN ACCESS
An Effective VHDL Implementation of IEEE 754 Floating Point
Unit using CLA and Rad-4 Modified Booth Encoder Multiplier
Subitha. M. B.
Assistant Professor, SNGCET Payyanur,Kerala.
ABSTRACT
Most of the signal processing algorithms using floating point arithmetic, which requires millions of operations
per second to be performed. For such stringent requirement design of fast, precise and efficient circuit is
needed. This article present an IEEE 754 floating point unit using carry look ahead adder and radix-4 modified
Booth encoder multiplier algorithm and the design is compared in terms of speed , area and power consumption.
The adder used here will increase the speed and the multiplier is used to reduce power consumption, area and
number of partial product get generated. The floating point unit design deals with the detection of exceptions
and trapped overflow and underflow exceptions as an integral part of the rounding unit. This work is used to
reduce area, power consumption and speed up the operations with more accurate results. The basic
methodology and approach are implemented in VHDL (Very Large Scale Integration Hardware Description
Language).
Keywords-FP multiplier, MBE.
I.
INTRODUCTION
Multiplication is one of the basic functions
used in digital signal processing (DSP). It requires
more hardware resources and processing time than
addition and subtraction. In fact, 8.72% of all
instructions in a typical processing unit are
multiplier. In computers, a typical central processing
unit devotes a considerable amount of processing
time in implementing arithmetic operations,
particularly multiplication operations. Most high
performance digital signal processing systems rely on
hardware multiplication to achieve high data
throughput.
Multiplication is an important
fundamental arithmetic operation. Multiplicationbased operations such as Multiply and Accumulate
(MAC) are currently implemented in many Digital
Signal Processing (DSP) applications such as
convolution, Fast Fourier Transform (FFT), filtering
and in microprocessors in its arithmetic and logic
unit. Since multiplication dominates the execution
time of most DSP algorithms, so there is a need of
high speed multiplier. Currently, multiplication time
is still that dominant factor in determining the
instruction cycle time of a DSP chip. The multiplier
is a fairly large block of a computing system. The
amount of circuitry involved is directly proportional
to square of its resolution i.e., a multiplier of size of n
bits has O(n2) gates. In the past, many novel ideas for
multipliers have been proposed to achieve high
performance. The demand for high speed processing
has been increasing as a result of expanding computer
and signal processing applications. Higher
throughput arithmetic operations are important to
www.ijera.com
achieve the desired performance in many real-time
signal and image processing applications. One of the
key arithmetic operations in such applications is
multiplication and the development of a multiplier
circuit has been a subject of interest over decades.
Reducing the time delay and power consumption are
very essential requirements for many applications.
This article presents a modified booth
encoder
multiplier
architecture.
Multiplier
architectures fall generally into two categories i.e.,
“tree” multipliers and “array” multipliers. Tree
multipliers add as many partial products in parallel as
possible and therefore, are very high performance
architectures. Unfortunately, tree multipliers are very
irregular, hard to layout and hence large. Array
multipliers, on the other hand, are very regular, small
in size, but suffer in latency and propagation delay.
Due to array organization, determining the
propagation delay of array multiplier is not straight
forward. Multiplier based on Modified Booth
algorithm and Wallace addition is one of the fast and
low power multiplier. The speed of modified Booth
encoder multiplier can further be increased by
pipelining.
II.
THE IEEE-754 STANDARD
FORMATS
IEEE 754 standard is a technical standard
established by IEEE and the most widely used
standard for floating-point computation, followed
by many hardware (CPU and FPU) and software
implementations. Single-precision floating-point
format is a computer number format that occupies 32
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bits in a computer memory and represents a wide
dynamic range of values by using a floating point. In
IEEE 754-2008, the 32-bit with base 2 format is
officially referred to as single precision or binary 32.
It was called single in IEEE 754-1985. The IEEE 754
standard specifies a single precision number as
having sign bit which is of 1 bit length, an exponent
of width 8 bits and a significant precision of 24
bits out of which 23 bits are explicitly stored and
1 bit is implicit 1.Sign bit determines the sign of the
number where 0 denotes a positive number and 1
denotes a negative number. It is the sign of the
mantissa as well. Exponent is an 8 bit signed integer
from 128 to 127 (2's Complement) or can be an 8 bit
unsigned integer from 0 to 255 which is the accepted
biased form in IEEE 754 single precision definition.
In this case an exponent with value 127 represents
actual zero. The true mantissa includes 23 fraction
bits to the right of the binary point and an implicit
leading bit (to the left of the binary point) with value
1 unless the exponent is stored with all zeros. Thus
only 23 fraction bits of the mantissa appear in the
memory format but the total precision is 24 bits.
The IEEE Standard for Binary Floating Point
Arithmetic ANSI/IEEE Std754-2008 is used.
Numbers in this format are composed of the
following three fields:
1-bit sign, S: A value of ‘1’ indicates that
the number is negative, and a ‘0’ indicates a positive
number.
Bias-127 exponent, e = E + bias: This gives us an
exponent range from Emin = -126 to Emax =127.
Mantissa, M: A twenty three bit fraction, a bit is
added to the fraction to form what is called the
significand. If the exponent is greater than 0 and
smaller than 255, and there is 1 in the MSB of the
significand then the number is said to be a
normalized number.
III.
FLOATING POINT
MULTIPLICATION ALGORITHM
Multiplying two numbers in floating point
format is done by
1-adding the exponent of the two numbers then
subtracting the bias from their result,
2-multiplying the significand of the two numbers,
and
3-calculating the sign by XORing the sign of the two
numbers. In order to represent the multiplication
result as a normalized number there should be 1 in
the MSB of the result (leading one).
To multiply two floating point numbers the
following is done:
1. Multiplying the significand(1.M1*1.M2 )
2. Placing the decimal point in the result
3. Adding the exponents; i.e.(E1 + E2 - Bias)
4. Obtaining the sign; i.e. s1 xor s2
5. Normalizing the result; i.e. obtaining 1 at the MSB
of the result’s significand.
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6. Rounding the result to fit in the available bits
7. Checking for underflow/overflow occurrence
Figure 1 shows the multiplier structure;
exponent’s addition, significand multiplication, and
result’s sign calculation are independent and are done
in parallel. The significand multiplication is done on
two 24 bit numbers and results in a 48 bit product,
which we will call the intermediate product (IP). The
IP is represented as (47 down to 0) and the decimal
point is located between bits 46 and 45 in the IP. The
following sections detail each block of the floating
point multiplier.
IV.
HARDWARE OF FP MULTIPLIER
Fig1: Block diagram of floating point multiplier
1 Sign bit calculation
Multiplying two numbers results in a
negative sign number if one of the multiplied
numbers is of a negative value. By the aid of a truth
table it find that this can be obtained by XORing the
sign of two inputs.
2 Unsigned adder / subtractor (for exponent
addition)
To reduce the delay caused by the effect
of carry propagation in the ripple carry adder, we
attempt to evaluate the carry-out for each stage (same
as carry-in to next stage) concurrently with the
computation of the sum bit. The two Boolean
functions for the sum and carry are as follows:
Sum = Ai Bi Ci
Cout =Ai. Bi +(Ai Bi)Ci
Let Gi = Ai · Bi be the carry generate function and
Pi = (Ai Bi) be the carry propagate function, Then
we can rewrite the carry function as follows:
Ci+1 = Gi + Pi · Ci
Thus, for 4-bit adder, we can compute the carry for
all the stages as shown below:
C1 = G0 + P0 · C0
C2 =G1+ P1·C1 = G1 + P1 · G0 + P1 · P0 · C0
C3= G2+P2·G1 + P2·P1·G0 +P2 · P1 · P0 · C0
C4=G3+P3·G2+P3·P2·G1+P3·P2·P1·G0+P3·P2.P1.P.
C0
In general, we can write:
The sum function: SUMi = Ai Bi Ci = Pi
Ci
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The carry function: Carry=Gi+Pi.Ci
Carry Look Ahead Adder can produce carries faster
due to parallel generation of the carry bits by
using additional circuitry.
3 Multiplier for unsigned data
Multiplication involves the generation of
partial products, one for each digit in the multiplier,
as in figure 2. These partial products are then
summed to produce the final product. The
multiplication of two n-bit binary integers results in a
product of up to 2n bits in length.
Multiplier
Multiplicand
Fig2:Block diagram of low power modified booth
multiplier
The Modified booth algorithm (MBA) or
Modified booth encoding (MBE) was proposed by
O.L Macsorley. The recording method is widely used
to generate partial products for implementation of
large parallel multipliers, which adopts the parallel
encoding scheme. One of the solutions of realizing
high speed multipliers is to enhance parallelism,
which helps to reduce subsequent stages. The original
booth algorithm (radix 2) had 2 drawbacks:
The number of add subtract operations and the
number of shift operations become variable and
become inconvenient for designing parallel
multipliers.
The algorithm becomes inefficient when there
are isolated 1’s.
These problems can be overcome by
modified booth algorithm. MBA process three bits at
a time during recoding. Recoding the multiplier in
higher radix is a powerful way to speed up standard
booth multiplication algorithm. In each cycle a
greater number of bits can be inspected and
eliminated therefore, total number of cycles required
to obtain products get reduced. Number of bits
inspected in radix r is given by n= 1+log2r. Algorithm
for Modified booth is given below. In each cycle of
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radix-4 algorithm, 3 bits are inspected and two are
eliminated. Procedure for implementing radix-4
algorithm is as follows,
Append a zero to the right of LSB.
Extend the sign bit 1 position if necessary to
ensure that n is even.
According to the value of each vector, find each
partial product.
Y
Y
Y
2i
2i
2i
+1
Recoded
Digit
Operand
Multiplication
-1
0
0
0
0
0
0
1
+1
0
1
0
+1
0
1
1
+2
1
0
0
-2
1
0
1
-1
1
1
0
-1
1
1
1
0
Table 1: Modified Booth Algorithm
0*Multiplicand
+1*Multiplicand
+1*Multiplicand
+2*Multiplicand
-2*Multiplicand
-1*Multiplicand
-1*Multiplicand
0*Multiplicand
Radix-4 encoding reduces the total number
of multiplier digits by a factor of two, which means
in this case the number of multiplier digits will
reduce from 16 to 8. Booth’s recoding method does
not propagate the carry into subsequent stages. This
algorithm groups the original multiplier into groups
of 3consecutive digits where the outer most digit in
each group is shared with the outer most digit of the
adjacent group. Each of these group of three binary
digits then corresponds to one of the numbers from
the set {+2,+1,0,-1,-2}. Each recoder produces a 3-bit
output where the 1st bit represents the number 1 and
the 2nd bit represent number 2. The 3rd and final bit
indicates whether the number in the 1st or 2nd bit is
negative.
4 Normalizer
The result of the significand multiplication
(intermediate product) must be normalized to have a
leading ‘1’ just to the left of the decimal point (i.e., in
the bit 46 in the intermediate product). Since the
inputs are normalized numbers then the intermediate
product has the leading one at bit 46 or 47.
1-If the leading one is at bit 46 (i.e., to the left of the
decimal point) then the intermediate product is
already a normalized number and no shift is needed.
2- If the leading one is at bit 47 then the intermediate
product is shifted to the right and the exponent is
incremented by 1.The shift operation is done using
combinational shift logic made by multiplexers.
5 Underflow/overflow detection
Overflow/underflow means that the result’s
exponent is too large/small to be represented in the
exponent field. The exponent of the result must be 8
bits in size, and must be between 1 and 254 otherwise
the value is not a normalized one. An overflow may
occur while adding the two exponents or during
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normalization. Overflow due to exponent addition
may be compensated during subtraction of the bias;
resulting in a normal output value (normal operation).
An underflow may occur while subtracting the bias to
form the intermediate exponent. If the intermediate
exponent < 0 then it’s an underflow that can never
be compensated; if the intermediate exponent= 0 then
it’s an underflow that may be compensated during
normalization by adding 1 to it. When an overflow
occurs an overflow flag signal goes high and the
result turns to ±Infinity .When an underflow occurs
an underflow flag signal goes high and the result
turns to ±Zero.
6 Pipelining the multiplier
In order to enhance the performance of the
multiplier, three pipelining stages are used to divide
the critical path thus increasing the maximum
operating frequency of the multiplier. The pipelining
stages are imbedded at the following locations:
1.In the middle of the significand multiplier, and in
the middle of the exponent adder
(before the
bias subtraction).
2.After the significand multiplier, and after the
exponent adder.
3.At the floating point multiplier outputs (sign,
exponent and mantissa bits).Figure 3 shows the
pipelining stages as dotted lines.
Three pipelining stages mean that there is latency in
the output by three clocks.
Fig3: Floating point multiplier with pipelined
stages
V.
COMPARISONS AND VHDL
SIMULATION
First the VHDL simulation of two
multipliers is considered. The VHDL code for both
multipliers, using a fast carry look-ahead adder are
generated. The multiplier uses 24-bit values. The
worst case was applied to the two multipliers, where
the gate delay is assumed to be 5ns. The array
multiplier has a delay of 28.713 ns with a total of
23.58 mW power consumption. The FP multiplier
using modified booth encoder multiplier has provided
a delay of 26.990ns with 23.6 mW of power
consumption. The whole multiplier was tested against
the Xilinx floating point multiplier core generated by
Xilinx coregen. Xilinx coregen was customized to
have two flags to indicate overflow and underflow ,
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and to have a maximum latency of three cycles.
Xilinx core implements the “round to nearest “
rounding mode. A test-bench is used to generate the
stimulus and applies it to the implemented floating
point multiplier and to the Xilinx core then compares
the result .The floating point multiplier was also
checked using precision synthesis tool targeted to
XC3S1500-5FG456 Spartan-3 device.
VI.
CONCLUSION AND FUTURE
WORK
The Floating point unit is designed. The
design is done in such a way that the floating point
unit can be effectively interfaced with any processor
that is either 32 bit or 64 bit. The architectures that
are used in the design are selected based on careful
analysis considering the three parameters, speed, area
and power consumption. The FP unit may extended
to quadruple precision format for more advanced and
scientific computations. As an attempt to develop,
IEEE 754 compatible floating point unit algorithm
and architecture level optimization techniques for low
power high-speed multiplier design, techniques
presented in this article. There are several future
research directions are possible as follows:
One possible direction is radix higher-than-4
recoding. Only radix-4 recoding is considered in this
article as it is a simple and popular choice. Higherradix recoding further reduces the number of PPs and
thus has the potential of power saving. In order to
enhance the performance, higher order compressors
like 7:2, 9:2 can be used to accumulate the partial
products. Deep level pipeline architecture can be used
for speed improvements. Multiplication intensive
applications, such as DSP or graphics, could benefit
significantly from several high performance
multipliers on the same chip. A single very high
throughput multiplier, or several multipliers working
in parallel on the same chip, could open up new
possibilities such as single chip video signal
processors.
REFERENCES
[1]
[2]
[3]
[4]
Amine Bermark, Guixuan Liang and
Qingzheng, “A High-speed 32-bit Signed/
Unsigned Pipelined Multiplier”, Department
of Electronics and Computer Engineering,
Honkong University of Science and
Technology, Hong Kong, China.
Cang-YuanGuo, Jiun-Ping Wang and
Shiann-Rong Kuang, Member IEEE
“Modified Booth Multipliers with Regular
Partial Product Array”, IEEE Transactions
on Circuits and Systems (2009).
Carl Hamacher, Zvonko Vranesic and
Safwat Zaky, “Computer Organization”,
Fifth edition.
Chris Babb, Jeff Blank, Ivan Castellanos and
John Moskal, “Floating Point Multiplier”,
ECE 587.
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