Imce: Energy-Efficient Bit-Wise In-Memory Convolution Engine For Deep Neural Network

Abstract

In this paper, we pave a novel way towards the concept of bit-wise In-Memory Convolution Engine (IMCE) that could implement the dominant convolution computation of Deep Convolutional Neural Networks (CNN) within memory. IMCE employs parallel computational memory sub-array as a fundamental unit based on our proposed Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) design. Then, we propose an accelerator system architecture based on IMCE to efficiently process low bit-width CNNs. This architecture can be leveraged to greatly reduce energy consumption dealing with convolutional layers and also accelerate CNN inference. The device to architecture co-simulation results show that the proposed system architecture can process low bit-width AlexNet on ImageNet data-set favorably with 785.25μJ/img, which consumes ∼3× less energy than that of recent RRAM based counterpart. Besides, the chip area is ∼4× smaller.

Publication Date

2-20-2018

Publication Title

Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Volume

2018-January

Number of Pages

111-116

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ASPDAC.2018.8297291

Socpus ID

85045299946 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85045299946

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