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Towards spike-based machine intelligence with neuromorphic computing
Nature ( IF 50.5 ) Pub Date : 2019-11-27 , DOI: 10.1038/s41586-019-1677-2 Kaushik Roy 1 , Akhilesh Jaiswal 1 , Priyadarshini Panda 1
Nature ( IF 50.5 ) Pub Date : 2019-11-27 , DOI: 10.1038/s41586-019-1677-2 Kaushik Roy 1 , Akhilesh Jaiswal 1 , Priyadarshini Panda 1
Affiliation
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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. The authors review the advantages and future prospects of neuromorphic computing, a multidisciplinary engineering concept for energy-efficient artificial intelligence with brain-inspired functionality.
中文翻译：
通过神经形态计算实现基于尖峰的机器智能
在类似大脑的“尖峰”计算框架的指导下，神经拟态计算——机器智能的大脑启发计算——有望实现人工智能，同时降低计算平台的能源需求。这个跨学科领域始于用于生物神经例程的硅电路的实现，但已经发展到包含基于尖峰编码和事件驱动表示的算法的硬件实现。在这里，我们概述了算法和硬件的神经形态计算的发展，并重点介绍了学习和硬件框架的基础知识。我们讨论了神经形态计算的主要挑战和未来前景，重点是算法-硬件协同设计。
更新日期：2019-11-27
中文翻译：
通过神经形态计算实现基于尖峰的机器智能
在类似大脑的“尖峰”计算框架的指导下，神经拟态计算——机器智能的大脑启发计算——有望实现人工智能，同时降低计算平台的能源需求。这个跨学科领域始于用于生物神经例程的硅电路的实现，但已经发展到包含基于尖峰编码和事件驱动表示的算法的硬件实现。在这里，我们概述了算法和硬件的神经形态计算的发展，并重点介绍了学习和硬件框架的基础知识。我们讨论了神经形态计算的主要挑战和未来前景，重点是算法-硬件协同设计。
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