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Towards spike-based machine intelligence with neuromorphic computing

Abstract

Guided by brain-like ‘spiking’ computational frameworks, neuromorphic computing—brain-inspired computing for machine intelligence—promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm–hardware codesign.

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Fig. 1: Key attributes of biological and silicon-based computing frameworks.
Fig. 2: Timeline of major discoveries and advances in intelligent computing, from the 1940s to the present6, 10, 14, 73, 78, 84, 93, 105, 115, 136,150,151.
Fig. 3: SNN computational models.
Fig. 4: Global and local-learning principles in spiking networks.
Fig. 5: Some representative ‘Big Brain’ chips and AER methods.
Fig. 6: The use of non-volatile memory devices as synaptic storage.

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Acknowledgements

We thank A. Sengupta (Pennsylvania State University), A. Raychowdhury (Georgia Institute of Technology) and S. Gupta (Purdue University) for their input. The work was supported in part by the Center for Brain-inspired Computing Enabling Autonomous Intelligence (C-BRIC), a DARPA-sponsored JUMP center, the Semiconductor Research Corporation, the National Science Foundation, Intel Corporation, the DoD Vannevar Bush Fellowship, the ONR-MURI programme, and the US Army Research Laboratory and the UK Ministry of Defence under agreement number W911NF-16-3-0001.

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Correspondence to Kaushik Roy.

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Roy, K., Jaiswal, A. & Panda, P. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019). https://doi.org/10.1038/s41586-019-1677-2

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