期刊
IEEE TRANSACTIONS ON NANOTECHNOLOGY
卷 14, 期 6, 页码 1013-1023出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNANO.2015.2437902
关键词
Artificial neural network; soft-limiting neuron; domain wall motion; memristor crossbar array
类别
资金
- National Science Foundation [CCF-1320808]
- DoD
- NSSEFF Fellows program
- Semiconductor Research Corporation
- DARPA UPSIDE program
Recent years have witnessed growing interest in the use of artificial neural networks (ANNs) for vision, classification, and inference problems. An artificial neuron sums N weighted inputs and passes the result through a non-linear transfer function. Large-scale ANNs impose very high computing requirements for training and classification, leading to great interest in the use of post-CMOS devices to realize them in an energy efficient manner. In this paper, we propose a spin-transfer-torque (STT) device based on domain wall motion (DWM) magnetic strip that can efficiently implement a soft-limiting non-linear neuron (SNN) operating at ultra-low supply voltage and current. In contrast to previous spin-based neurons that can only realize hard-limiting transfer functions, the proposed STT-SNN displays a continuous resistance change with varying input current, and can therefore be employed to implement a soft-limiting neuron transfer function. Soft-limiting neurons are greatly preferred to hard-limiting ones due to their much improved modeling capacity, which leads to higher network accuracy and lower network complexity. We also present an ANN hardware design employing the proposed STT-SNNs and memristor crossbar arrays (MCA) as synapses. The ultra-low voltage operation of the magneto metallic STT-SNN enables the programmable MCA-synapses, computing analog-domain weighted summation of input voltages, to also operate at ultra-low voltage. We modeled the STT-SNN using micro-magnetic simulation and evaluated them using an ANN for character recognition. Comparisons with analog and digital CMOS neurons show that STT-SNNs can achieve around two orders of magnitude lower energy consumption.
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