Dima: A Depthwise Cnn In-Memory Accelerator

Abstract

In this work, we first propose a deep depthwise Convolutional Neural Network (CNN) structure, called Add-Net, which uses binarized depthwise separable convolution to replace conventional spatial-convolution. In Add-Net, the computationally expensive convolution operations (i.e. Multiplication and Accumulation) are converted into hardware-friendly Addition operations. We meticulously investigate and analyze the Add-Net's performance (i.e. accuracy, parameter size and computational cost) in object recognition application compared to traditional baseline CNN using the most popular large scale ImageNet dataset. Accordingly, we propose a Depthwise CNN In-Memory Accelerator (DIMA) based on SOT-MRAM computational sub-arrays to efficiently accelerate Add-Net within non-volatile MRAM. Our device-to-architecture co-simulation results show that, with almost the same inference accuracy to the baseline CNN on different data-sets, DIMA can obtain ∼1.4× better energy-efficiency and 15.7× speedup compared to ASICs, and, ∼1.6× better energy-efficiency and 5.6× speedup over the best processing-in-DRAM accelerators.

Publication Date

11-5-2018

Publication Title

IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3240765.3240799

Socpus ID

85058159102 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85058159102

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