Cmp-Pim: An Energy-Efficient Comparator-Based Processing-In-Memory Neural Network Accelerator

Abstract

In this paper, an energy-efficient and high-speed comparator-based processing-in-memory accelerator (CMP-PIM) is proposed to efficiently execute a novel hardware-oriented comparator-based deep neural network called CMPNET. Inspired by local binary pattern feature extraction method combined with depthwise separable convolution, we first modify the existing Convolutional Neural Network (CNN) algorithm by replacing the computationally-intensive multiplications in convolution layers with more efficient and less complex comparison and addition. Then, we propose a CMP-PIM that employs parallel computational memory sub-array as a fundamental processing unit based on SOT-MRAM. We compare CMP-PIM accelerator performance on different data-sets with recent CNN accelerator designs. With the close inference accuracy on SVHN data-set, CMP-PIM can get ∼ 94× and 3× better energy efficiency compared to CNN and Local Binary CNN (LBCNN), respectively. Besides, it achieves 4.3× speed-up compared to CNN-baseline with identical network configuration.

Publication Date

6-24-2018

Publication Title

Proceedings - Design Automation Conference

Volume

Part F137710

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3195970.3196009

Socpus ID

85053683815 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85053683815

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