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Neuro-inspired computing chips
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DOI：10.1038/s41928-020-0435-7.png)
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Abstract
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The rapid development of artificial intelligence (AI) demands the rapid development of domain-specific hardware specifically designed for AI applications. Neuro-inspired computing chips integrate a range of features inspired by neurobiological systems and could provide an energy-efficient approach to AI computing workloads. Here, we review the development of neuro-inspired computing chips, including artificial neural network chips and spiking neural network chips. We propose four key metrics for benchmarking neuro-inspired computing chips - computing density, energy efficiency, computing accuracy, and on-chip learning capability - and discuss co-design principles, from the device to the algorithm level, for neuro-inspired computing chips based on non-volatile memory. We also provide a future electronic design automation tool chain and propose a roadmap for the development of large-scale neuro-inspired computing chips. This Review Article examines the development of neuro-inspired computing chips and their key benchmarking metrics, providing a co-design tool chain and proposing a roadmap for future large-scale chips.
Keywords:
MEMRISTOR
NETWORKS
INTELLIGENCE
PROCESSOR
INFERENCE
SYSTEM
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