MARS: Multimacro Architecture SRAM CIM-Based Accelerator With Co-Designed Compressed Neural Networks

Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the large storage overheads and the substantial computational cost of CNNs are problematic in hardware accelerators. Computing-in-memory (CIM) architecture has demonstrated great potential to effectively com...

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Published in:IEEE transactions on computer-aided design of integrated circuits and systems Vol. 41; no. 5; pp. 1550 - 1562
Main Authors: Sie, Syuan-Hao, Lee, Jye-Luen, Chen, Yi-Ren, Yeh, Zuo-Wei, Li, Zhaofang, Lu, Chih-Cheng, Hsieh, Chih-Cheng, Chang, Meng-Fan, Tang, Kea-Tiong
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Co-design
Common Information Model (computing)
Compression algorithm
Computer architecture
Computing costs
computing-in-memory (CIM)
deep learning
Design
Hardware
Machine learning
Multiplication
Neural networks
quantization
Quantization (signal)
Random access memory
Software
Static random access memory
Training
ISSN:0278-0070, 1937-4151
Online Access:Get full text
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Summary:Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the large storage overheads and the substantial computational cost of CNNs are problematic in hardware accelerators. Computing-in-memory (CIM) architecture has demonstrated great potential to effectively compute large-scale matrix-vector multiplication. However, the intensive multiply and accumulation (MAC) operations executed on CIM macros remain bottlenecks for further improvement of energy efficiency and throughput. To reduce computational costs, model compression is a widely studied method to shrink the model size. For implementation in a static random access memory (SRAM) CIM-based accelerator, the model compression algorithm must consider the hardware limitations of CIM macros. In this study, a software and hardware co-design approach is proposed to design MARS, a SRAM-based CIM (SRAM CIM)-based CNN accelerator that can utilize multiple SRAM CIM macros as processing units and support a sparse CNN, and an SRAM CIM-aware model compression algorithm that considers a CIM architecture to reduce the number of network parameters. With the proposed hardware software co-designed method, MARS can reach over 700 and 400 FPS for CIFAR-10 and CIFAR-100, respectively. In addition, MARS achieves 52.3 and 88.2 TOPs/W in VGG16 and ResNet18, respectively.
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ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2021.3082107