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A Case for Lifetime Reliability-Aware Neuromorphic Computing
Conference proceeding   Open access

A Case for Lifetime Reliability-Aware Neuromorphic Computing

Shihao Song, Anup Das and IEEE
2020 IEEE 63RD INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS), v 2020-, pp 596-598
01 Jan 2020
DOI: https://doi.org/10.1109/MWSCAS48704.2020.9184557
url
http://arxiv.org/abs/2007.02210View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Computer Science Computer Science, Theory & Methods Engineering Engineering, Electrical & Electronic Science & Technology Technology
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.

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17 citations in Web of Science
9 citations in Scopus

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Web of Science research areas
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
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