This document discusses cognitive power management in electronic systems. It notes that power is a key challenge as embedded memories are dominant consumers accounting for 50% of chip area and power. The presented approach creates statistical models of channel and hardware noise. It develops error-correcting codes to handle combined channel and noise and a framework to explore power management schemes by propagating error statistics. This approach can manage memory and logic power consumption by modulating supply voltage based on channel and system conditions. The concepts are demonstrated experimentally and with MATLAB simulations.
It's my ppt of disseration defense at 2012_06_04. Please give me some feedback so I can improve my ppt skills. Feel free to discuss any problem with me. Thank you!
it is used for security purpose using two level dct and wavelet packet denoising .based on digital image processing.the software based on matlab.it is used for high security purpose.
It's my ppt of disseration defense at 2012_06_04. Please give me some feedback so I can improve my ppt skills. Feel free to discuss any problem with me. Thank you!
it is used for security purpose using two level dct and wavelet packet denoising .based on digital image processing.the software based on matlab.it is used for high security purpose.
LDPC codes have been discovered a long time ago & re-discovered after invention of turbo codes. These two codes are actors of revolution of error correcting codes theory.
In this thesis, the principle of LDPC codes will be studied.
Besides, based on this, design is done for the IP core, involves
LDPC code performance and construction of behavioural model for Encoder & Decoder using Generator matrix and parity check matrix , then use Model sim for compilation & simulation also test bench design is made to test Encoder & Decoder blocks.
LDPC Encoding is explained in this ppt. for MATLAB code and more information you can visit link given below:
http://www.slideshare.net/bhagwatsinghmahecha/itc-final-report
LDPC - Encoding
LDPC code is a linear error correcting code, a method of transmitting a message over a noisy transmission channel. An LDPC is constructed using a sparse bipartite graph.
In our Project:
Encoding a LDPC code was done in Matlab hardware implementation was done on FPGA-Field ProgrammableGate-Array using Verilog
Structure 2014 - The strategic value of the cloud - Joe WeinmanGigaom
Presentation from Gigaom's Structure 2014 conference, June 21-22 in San Francisco
The strategic value of the cloud
Joe Weinman, Author, Cloudonomics
Chairman, IEEE Intercloud Testbed Executive Committee
#gigaomlive
More at http://events.gigaom.com/structure-2014/
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2016-member-meeting-mit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vivienne Sze, Assistant Professor at MIT, delivers the presentation "Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks" at the September 2016 Embedded Vision Alliance Member Meeting. Sze describes the results of her team's recent research on optimized hardware for deep learning.
Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Im...Wesley De Neve
Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Images to Privacy Leakage. Presentation given at the 10th International Workshop on Digital Forensics and Watermarking (IWDW'11).
Note that a more extensive objective and subjective study of privacy protection in video surveillance systems can be found in the following book chapter:
H. Sohn, D. Lee, W. De Neve, K.N. Plataniotis, and Y.M. Ro. An objective and subjective evaluation of content-based privacy protection of face images in video surveillance systems using JPEG XR. Effective Surveillance for Homeland Security: Balancing Technology and Social Issues. CRC Press / Taylor & Francis. May 2013. pp. 111-140.
http://www.citeulike.org/user/wmdeneve/article/10831550
http://www.crcpress.com/product/isbn/9781439883242
Oliver Holland - IEEE VTS UKRI - Energy efficiency challenges of data volume...Keith Nolan
Oliver Holland from King's College London talks about energy efficiency challenges of data volume increases and the use of sleep modes facilitated by opportunistic cognitive radio networking as a solution
Identification of Causal Variables for Building Energy Fault Detection by Sem...Keigo Yoshida
K. Yoshida, M. Inui, T. Yairi, K. Machida, M. Shioya, and Y. Masukawa, "Identification of Causal Variables for Building Energy Fault Detection by Semi-supervised LDA and Decision Boundary Analysis", in Proc. ICDM Workshops, 2008, pp.164-173.
Identification of Causal Variables for Building Energy Fault Detection by Sem...sudare
K. Yoshida, M. Inui, T. Yairi, K. Machida, M. Shioya, and Y. Masukawa, "Identification of Causal Variables for Building Energy Fault Detection by Semi-supervised LDA and Decision Boundary Analysis", in Proc. ICDM Workshops, 2008, pp.164-173.
Abstract:
This paper addresses the identification problem of causal variables for the system anomaly. In real-world complicated systems, even experts often fail to specify causal factors, thus they attempt to detect the anomaly with exploratory heuristics. Our goal is to offer further information that supports anomaly cause analysis using the incomplete empirical knowledge. Proposed technique discovers responsible factors for the fault by leveraging domain knowledge with an effective combination of semi-supervised linear discriminant analysis (LDA) and boundary-based discriminative subspace identification method. Experimental results on synthetic and real dataset confirmed validity of our approach. Moreover, we applied this method to the building energy fault diagnosis and succeeded in extracting causal variables for energy waste in a building.
Survey of up and coming technologies and issues facing designers, builders and users of industrial automation and systems across all technologies. (CMAFH) Drive for Technology 2010 presentation
LDPC codes have been discovered a long time ago & re-discovered after invention of turbo codes. These two codes are actors of revolution of error correcting codes theory.
In this thesis, the principle of LDPC codes will be studied.
Besides, based on this, design is done for the IP core, involves
LDPC code performance and construction of behavioural model for Encoder & Decoder using Generator matrix and parity check matrix , then use Model sim for compilation & simulation also test bench design is made to test Encoder & Decoder blocks.
LDPC Encoding is explained in this ppt. for MATLAB code and more information you can visit link given below:
http://www.slideshare.net/bhagwatsinghmahecha/itc-final-report
LDPC - Encoding
LDPC code is a linear error correcting code, a method of transmitting a message over a noisy transmission channel. An LDPC is constructed using a sparse bipartite graph.
In our Project:
Encoding a LDPC code was done in Matlab hardware implementation was done on FPGA-Field ProgrammableGate-Array using Verilog
Structure 2014 - The strategic value of the cloud - Joe WeinmanGigaom
Presentation from Gigaom's Structure 2014 conference, June 21-22 in San Francisco
The strategic value of the cloud
Joe Weinman, Author, Cloudonomics
Chairman, IEEE Intercloud Testbed Executive Committee
#gigaomlive
More at http://events.gigaom.com/structure-2014/
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2016-member-meeting-mit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vivienne Sze, Assistant Professor at MIT, delivers the presentation "Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks" at the September 2016 Embedded Vision Alliance Member Meeting. Sze describes the results of her team's recent research on optimized hardware for deep learning.
Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Im...Wesley De Neve
Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Images to Privacy Leakage. Presentation given at the 10th International Workshop on Digital Forensics and Watermarking (IWDW'11).
Note that a more extensive objective and subjective study of privacy protection in video surveillance systems can be found in the following book chapter:
H. Sohn, D. Lee, W. De Neve, K.N. Plataniotis, and Y.M. Ro. An objective and subjective evaluation of content-based privacy protection of face images in video surveillance systems using JPEG XR. Effective Surveillance for Homeland Security: Balancing Technology and Social Issues. CRC Press / Taylor & Francis. May 2013. pp. 111-140.
http://www.citeulike.org/user/wmdeneve/article/10831550
http://www.crcpress.com/product/isbn/9781439883242
Oliver Holland - IEEE VTS UKRI - Energy efficiency challenges of data volume...Keith Nolan
Oliver Holland from King's College London talks about energy efficiency challenges of data volume increases and the use of sleep modes facilitated by opportunistic cognitive radio networking as a solution
Identification of Causal Variables for Building Energy Fault Detection by Sem...Keigo Yoshida
K. Yoshida, M. Inui, T. Yairi, K. Machida, M. Shioya, and Y. Masukawa, "Identification of Causal Variables for Building Energy Fault Detection by Semi-supervised LDA and Decision Boundary Analysis", in Proc. ICDM Workshops, 2008, pp.164-173.
Identification of Causal Variables for Building Energy Fault Detection by Sem...sudare
K. Yoshida, M. Inui, T. Yairi, K. Machida, M. Shioya, and Y. Masukawa, "Identification of Causal Variables for Building Energy Fault Detection by Semi-supervised LDA and Decision Boundary Analysis", in Proc. ICDM Workshops, 2008, pp.164-173.
Abstract:
This paper addresses the identification problem of causal variables for the system anomaly. In real-world complicated systems, even experts often fail to specify causal factors, thus they attempt to detect the anomaly with exploratory heuristics. Our goal is to offer further information that supports anomaly cause analysis using the incomplete empirical knowledge. Proposed technique discovers responsible factors for the fault by leveraging domain knowledge with an effective combination of semi-supervised linear discriminant analysis (LDA) and boundary-based discriminative subspace identification method. Experimental results on synthetic and real dataset confirmed validity of our approach. Moreover, we applied this method to the building energy fault diagnosis and succeeded in extracting causal variables for energy waste in a building.
Survey of up and coming technologies and issues facing designers, builders and users of industrial automation and systems across all technologies. (CMAFH) Drive for Technology 2010 presentation
Structure 2014 - The right and wrong way to scale - RackspaceGigaom
Presentation from Gigaom's Structure 2014 conference, June 21-22 in San Francisco
The right and wrong way to scale
Taylor Rhodes, CEO, Rackspace
#gigaomlive
More at http://events.gigaom.com/structure-2014/
Structure 2014 - The future of cloud computing survey resultsGigaom
Presentation from Gigaom's Structure 2014 conference, June 21-22 in San Francisco
The future of cloud computing survey results
#gigaomlive
More at http://events.gigaom.com/structure-2014/
Gigaom's Structure 2014 conference, June 21-22 in San Francisco Launchpad company profiles
#gigaomlive
More at http://events.gigaom.com/structure-2014/
Structure 2014 - Disrupting the data center - Intel sponsor workshopGigaom
Presentation from Gigaom's Structure 2014 conference, June 21-22 in San Francisco
Intel sponsor workshop: Disrupting the data center
#gigaomlive
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Presentation from Gigaom's Structure 2014 conference, June 21-22 in San Francisco
Quantifying the iot
Craig Labovitz, Co-Founder and CEO, DeepField
#gigaomlive
More at http://events.gigaom.com/structure-2014/
25 Favorite Experiences in Tech - from Roadmap 2013Gigaom
Presentation from
Shoshana Berger, IDEO
Josh Brewer, Twitter
Ryan Freitas, about.me
Julie Horvath, GitHub
Braden Kowitz, Google Ventures
#roadmap2013
More at http://events.gigaom.com/roadmap-2013/
6. The Power Challenge
Power density limit of handheld
Logic vs memory power
(ITRS)
3
Wednesday, November 7, 12
7. The Power Challenge
Power density limit of handheld
Logic vs memory power
(ITRS)
3
Wednesday, November 7, 12
8. The Power Challenge
Power density limit of handheld
1. Power is the key Logic vs memory power
2. Embedded memories are
(ITRS)
dominant
3
Wednesday, November 7, 12
9. Concept System Model
Noisy
Noisy Wireless
Wireless Receiver
Channel
Noise is
uncontrollable and is
a function of the Errors are controllable!
environment. Errors and power
consumption are
inversely related.
Current Design Approach
Specs
Overdriven Vdd
Nominal Vdd
Low Vdd
Memory typically consumes
Aggressively approximately
Low Vdd Memory Array 50% of the chip area and/or power
x
Management Cycle
System Design: Circuit Design: Memory noise and power ü Observe the channel statistics and system state
Assume worst case
wireless conditions
Minimize noise at consumption can be directly üTake the action and modulate the supply voltage
expense of power
controlled by the supply voltage ü Monitor performance metrics such as BER, PSNR and
Wednesday, November 7, 12
10. CPM Approach
n Created a statistical model for both
Channel+HW noise
n Created a new class of FEC decoders for
combined Channel and hardware noise
q Viterbi, Turbo, LDPC
q Negligible hardware overhead 0.013%-0.65%
n Created a design exploration framework
for designers to easily experiment with
different power management schemes by
propagating error statistics through the
system.
n Approach is not limited to data path
memories but can be extended to control
memories and logic with some
modifications.
Wednesday, November 7, 12
11. CPM Approach
n Created a statistical model for both
Channel+HW noise
n Created a new class of FEC decoders for
combined Channel and hardware noise
q Viterbi, Turbo, LDPC
q Negligible hardware overhead 0.013%-0.65%
n Created a design exploration framework
for designers to easily experiment with
different power management schemes by
propagating error statistics through the
system.
n Approach is not limited to data path
memories but can be extended to control
memories and logic with some
modifications.
Wednesday, November 7, 12