research-article

Approximate Memory with Approximate DCT

Authors:

Shenghou Ma,

Paul AmpaduAuthors Info & Claims

GLSVLSI '19: Proceedings of the 2019 Great Lakes Symposium on VLSI

Pages 355 - 358

https://doi.org/10.1145/3299874.3318036

Published: 13 May 2019 Publication History

Get Access

Abstract

Approximate Computing is an emerging computing paradigm where one exploits inherent error resilience of certain applications (e.g., digital signal processing, multimedia and artificial intelligence) and trades off absolute computation precisions for performance, power, area, and/or efficiency gains. Approximate memory is a sub-branch of general approximate computing focusing on memory systems that trade off perfect data fidelity for increased storage density and reduced energy consumption for both memory and IO. This paper presents a new way to achieve approximate memory for natural sampled data (sound, sensor data, image and video frames) by applying the well known discrete cosine transformation (DCT) in one dimension between the last level cache and main memory to reduce memory traffic while maintaining acceptable quality degradations. By using state-of-the-art approximate DCT transformations, the proposed scheme achieves 20.3% of storage area at 37dB PSNR and SSIM = 0.99. If further quality degradation up to SSIM ≈ 0.9 is allowed, the storage saving can be up to 40%. The critical path of the circuit is comparable to state of the art approximate memory schemes, with only 3 levels of 8-bit adders (14 adders per 8-byte data block).

References

[1]

Wen-Hsiung Chen, C. Smith, and S. Fralick. 1977. A Fast Computational Algorithm for the Discrete Cosine Transform. IEEE Transactions on Communications, Vol. 25, 9 (September 1977), 1004--1009.

Google Scholar

[2]

Thiago L. T. da Silveira, Raíza S. Oliveira, Fábio M. Bayer, Renato J. Cintra, and Arjuna Madanayake. 2017. Multiplierless 16-point DCT Approximation for Low-complexity Image and Video Coding. Signal, Image and Video Processing, Vol. 11, 2 (01 Feb 2017), 227--233.

Google Scholar

[3]

Pete Fraser et. al. {n. d.}. The New Test Images: Image Compression Benchmark. http://imagecompression.info/test_images/. Accessed: 2018-12-13.

Google Scholar

[4]

Fei-Fei, R. Fergus and P. Perona. 2004. Learning Generative Visual Models from few Training Examples: an Incremental Bayesian Approach Tested on 101 Object Categories. In Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition (CVPR '04).

Digital Library

Google Scholar

[5]

E. Feig and S. Winograd. 1992. Fast Algorithms for the Discrete Cosine Transform. IEEE Transactions on Signal Processing, Vol. 40, 9 (Sept 1992), 2174--2193.

Digital Library

Google Scholar

[6]

D. T. Nguyen, H. Kim, H. Lee, and I. Chang. 2018. An Approximate Memory Architecture for a Reduction of Refresh Power Consumption in Deep Learning Applications. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS). 1--5.

Google Scholar

[7]

A. Pedram, S. Richardson, M. Horowitz, S. Galal, and S. Kvatinsky. 2017. Dark Memory and Accelerator-Rich System Optimization in the Dark Silicon Era. IEEE Design Test, Vol. 34, 2 (April 2017), 39--50.

Crossref

Google Scholar

[8]

U. S. Potluri, A. Madanayake, R. J. Cintra, F. M. Bayer, S. Kulasekera, and A. Edirisuriya. 2014. Improved 8-Point Approximate DCT for Image and Video Compression Requiring Only 14 Additions. IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 61, 6 (June 2014), 1727--1740.

Crossref

Google Scholar

[9]

A. Ranjan, A. Raha, V. Raghunathan, and A. Raghunathan. 2017. Approximate Memory Compression for Energy-Efficiency. In 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED). 1--6.

Google Scholar

[10]

L. Yang and B. Murmann. 2017. Approximate SRAM for Energy-Efficient, Privacy-Preserving Convolutional Neural Networks. In 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). 689--694.

Google Scholar

[11]

H. Zhao, L. Xue, P. Chi, and J. Zhao. 2017. Approximate Image Storage with Multi-level Cell STT-MRAM Main Memory. In 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) . 268--275.

Digital Library

Google Scholar

Cited By

View all

Radünz APortella LOliveira RBayer FCintra R(2023)Extensions on Low-Complexity DCT Approximations for Larger Blocklengths Based on Minimal Angle SimilarityJournal of Signal Processing Systems10.1007/s11265-023-01848-w95:4(495-516)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s11265-023-01848-w
Isquierdo MSoares RSampaio FZatt BPalomino D(2023)VVC decoder intra prediction using approximate storage: an error resilience evaluationAnalog Integrated Circuits and Signal Processing10.1007/s10470-023-02189-1117:1-3(95-111)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1007/s10470-023-02189-1
Ma SAmpadu P(2019)Optimal SAT-based Minimum Adder Synthesis of Linear Transformations2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS)10.1109/MWSCAS.2019.8885033(335-338)Online publication date: Aug-2019
https://doi.org/10.1109/MWSCAS.2019.8885033

Index Terms

Approximate Memory with Approximate DCT
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded hardware
    2. System on a chip

Recommendations

Approximate Memristive In-memory Computing
Special Issue ESWEEK 2017, CASES 2017, CODES + ISSS 2017 and EMSOFT 2017

The bottleneck between the processing elements and memory is the biggest issue contributing to the scalability problem in computing. In-memory computation is an alternative approach that combines memory and processor in the same location, and eliminates ...
Approximate storage in solid-state memories
MICRO-46: Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture

Memories today expose an all-or-nothing correctness model that incurs significant costs in performance, energy, area, and design complexity. But not all applications need high-precision storage for all of their data structures all of the time. This ...
Approximate Similarity-Aware Compression for Non-Volatile Main Memory
Abstract
Image bitmaps, i.e., data containing pixels and visual perception, have been widely used in emerging applications for pixel operations while consuming lots of memory space and energy. Compared with legacy DRAM (dynamic random access memory), non-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

GLSVLSI '19: Proceedings of the 2019 Great Lakes Symposium on VLSI

May 2019

562 pages

ISBN:9781450362528

DOI:10.1145/3299874

General Chairs:
Houman Homayoun
George Mason University, USA
,
Baris Taskin
Drexel University, USA
,
Program Chairs:
Tinoosh Mohsenin
UMBC, USA
,
Weisheng Zhao
Beihang University, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GLSVLSI '19

Sponsor:

SIGDA

GLSVLSI '19: Great Lakes Symposium on VLSI 2019

May 9 - 11, 2019

VA, Tysons Corner, USA

Acceptance Rates

Overall Acceptance Rate 312 of 1,156 submissions, 27%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
139
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Radünz APortella LOliveira RBayer FCintra R(2023)Extensions on Low-Complexity DCT Approximations for Larger Blocklengths Based on Minimal Angle SimilarityJournal of Signal Processing Systems10.1007/s11265-023-01848-w95:4(495-516)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s11265-023-01848-w
Isquierdo MSoares RSampaio FZatt BPalomino D(2023)VVC decoder intra prediction using approximate storage: an error resilience evaluationAnalog Integrated Circuits and Signal Processing10.1007/s10470-023-02189-1117:1-3(95-111)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1007/s10470-023-02189-1
Ma SAmpadu P(2019)Optimal SAT-based Minimum Adder Synthesis of Linear Transformations2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS)10.1109/MWSCAS.2019.8885033(335-338)Online publication date: Aug-2019
https://doi.org/10.1109/MWSCAS.2019.8885033

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Approximate Memristive In-memory Computing

Approximate storage in solid-state memories

Approximate Similarity-Aware Compression for Non-Volatile Main Memory