SOT-MRAM-based Binary Neural Networks Demonstration for Single Character Recognition

Author:

Zhao Dongyan1,Wang Yubo1,Chen Yanning12,Shao Jin1,Fu Zhen2,Liu Fang2,Bai Yue3,Zhao Faqiang1,Zhong Mingchen2,Pan Cheng2,Dong Yi3,Cao Kaihua3ORCID

Affiliation:

1. State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart-Chip Microelectronics, Technology Co., Ltd., Beijing 100192, P. R. China

2. Beijing Chip Identification, Technology Co., Ltd., Beijing 102200, P. R. China

3. School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, P. R. China

Abstract

With the substantial increase in the amount of data, the mismatch between the processing speed of the hardware and the software results in the ‘Memory Wall’ problem. Processing-in-memory (PIM), in which the compute and memory units are integrated, can avoid frequent data transmission. Binary neural network (BNN) uses binary weights and activations instead of full-precision weights and activations in the convolutional neural network, which reduces computational complexity with minor influence on accuracy. In this paper, we used a one-step operation to write a pair of SOT-MRAM cells and verified the two basic operations in BNN: XNOR and bitcount. Then, we employed an external control circuit with FPGA and accomplished ‘I’ single-character recognition based on vector-matrix multiplication in the SOT-MRAM array.

Funder

The Laboratory Open Fund of Beijing Smart-chip Microelectronics Technology Co., Ltd.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

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