2020/07/04 by Shihao Song, Anup Das, Song, Shihao +1
Computer Science · #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Neural and Evolutionary Computing (cs.NE) #cs.AR #cs.NE
paper · pdf · doi:10.48550/arxiv.2007.02210
4 pages, 6 figures, accepted at MWCAS 2020
arxiv created 2020/07/04 · arxiv updated 2020/07/07
Neuromorphic computing with non-volatile memory (NVM) can significantly improve performance and lower energy consumption of machine learning tasks implemented using spike-based computations and bio-inspired learning algorithms. High voltages required to operate certain NVMs such as phase-change memory (PCM) can accelerate aging in a neuron's CMOS circuit, thereby reducing the lifetime of neuromorphic hardware. In this work, we evaluate the long-term, i.e., lifetime reliability impact of executing state-of-the-art machine learning tasks on a neuromorphic hardware, considering failure models such as negative bias temperature instability (NBTI) and time-dependent dielectric breakdown (TDDB). Based on such formulation, we show the reliability-performance trade-off obtained due to periodic relaxation of neuromorphic circuits, i.e., a stop-and-go style of neuromorphic computing.