Energy-efficient Design of MTJ-based Neural Networks with Stochastic Computing

ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS

Article

Mondal, Ankit¹  Srivastava, Ankur¹ 

[1] Univ Maryland, Dept Elect & Comp Engn, 8223 Paint Branch Dr,AV Williams Bldg, College Pk, MD 20742 USA.

关键词: Magnetic tunnel junctions;    energy efficiency;    approximate computing;    regularization;   

DOI  :  10.1145/3359622

来源: SCIE

【 摘 要 】

Hardware implementations of Artificial Neural Networks (ANNs) using conventional binary arithmetic units are computationally expensive, energy-intensive, and have large area overheads. Stochastic Computing (SC) is an emerging paradigm that replaces these conventional units with simple logic circuits and is particularly suitable for fault-tolerant applications. We propose an energy-efficient use of Magnetic Tunnel Junctions (MTJs), a spintronic device that exhibits probabilistic switching behavior, as Stochastic Number Generators (SNGs), which forms the basis of our NN implementation in the SC domain. Further, the error resilience of target applications of NNs allows approximating the synaptic weights in our Mil-based NN implementation, in ways brought about by properties of the MTJ-SNG, to achieve energy-efficiency. An algorithm is designed that, given an error tolerance, can perform such approximations in a single-layer NN in an optimal way owing to the convexity of the problem formulation. We then use this algorithm and develop a heuristic approach for approximating multi-layer NNs. Classification problems were evaluated on the optimized NNs and results showed substantial savings in energy for little loss in accuracy.

【 授权许可】

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