An Energy-Efficient Accelerator with Relative-Indexing Memory for Sparse Compressed Convolutional Neural Network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Deep convolutional neural networks (CNNs) are widely used in image recognition and feature classification. However, deep CNNs are hard to be fully deployed for edge devices due to both computation-intensive and memory-intensive workloads. The energy efficiency of CNNs is dominated by off-chip memory accesses and convolution computation. In this paper, an energy-efficient accelerator is proposed for sparse compressed CNNs by reducing DRAM accesses and eliminating zero-operand computation. Weight compression is utilized for sparse compressed CNNs to reduce the required memory capacity/bandwidth and a large portion of connections. Thus, ReLU function produces zero-valued activations. Additionally, the workloads are distributed based on channels to increase the degree of task parallelism, and all-row-to-all-row non-zero element multiplication is adopted for skipping redundant computation. The simulation results over the dense accelerator show that the proposed accelerator achieves 1.79x speedup and reduces 23.51%, 69.53%, 88.67% on-chip memory size, energy, and DRAM accesses of VGG-16.

Original languageEnglish
Title of host publicationProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages42-45
Number of pages4
ISBN (Electronic)9781538678848
DOIs
StatePublished - Mar 2019
Event1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan
Duration: 18 Mar 201920 Mar 2019

Publication series

NameProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

Conference

Conference1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
CountryTaiwan
CityHsinchu
Period18/03/1920/03/19

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