Hritom Das;Rocco D. Febbo;Charles P. Rizzo;Nishith N. Chakraborty;James S. Plank;Garrett S. Rose
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引用次数: 0
Abstract
The synapse is a key element of neuromorphic computing in terms of efficiency and accuracy. In this paper, an optimized current-controlled memristive synapse circuit is proposed. Our proposed synapse demonstrates reliability in the face of process variation and the inherent stochastic behavior of memristors. Up to an 82% energy optimization can be seen during the SET operation over prior work. In addition, the READ process shows up to 54% energy savings. Our current-controlled approach also provides more reliable programming over traditional programming methods. This design is demonstrated with a 4-bit memory precision configuration. Using a spiking neural network (SNN), a neuromorphic application analysis was performed with this precision configuration. Our optimized design showed up to a 82% improvement in control applications and a 2.7x improvement in classification applications compared with other design cases.
期刊介绍:
The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.