Neuromorphic Computing with Phase Change, Device Reliability, and Variability Challenges

Neuromorphic computing with analog memory can accelerate deep neural networks (DNNs) by enabling multiply-accumulate (MAC) operations to occur within memory. Analog memory, however, presents a number of device-level challenges having macro-implications on the achievable accuracy and reliability of these artificial neural networks. This paper focuses on the adverse effects of conductance drift in phase-change memory (PCM) on network reliability. It is shown that conductance drift can be effectively compensated in a variety of networks by applying a `slope correction' technique to the squashing functions to maintain accuracy/reliability for a period of ~1 year. In addition to conductance drift, PCM poses considerable variability challenges, which impact the accuracy of the initial t
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weights. This paper summarizes recent advances in optimizing t
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weight programming, and provides evidence suggesting that the combination of `slope correction' and programming optimization techniques may allow DNN acceleration using analog memory while maintaining software-equivalent accuracy with reasonable reliability.