Designing bio-inspired autonomous error-tolerant massively parallel computing architectures

Liu, Lizheng; Liu, Yi; Ma, Ning; Zou, Zhuo; Zheng, Lirong

doi:10.1109/socc.2017.8226057

Cited by 3 publications

(1 citation statement)

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“…For instance, the principles of evolution and natural selection gave rise to evolutionary hardware for implementation of reconfigurable fault tolerant systems [10,58,151]. Closer to the brain, we have seen researchers exploit neuroplasticity mechanisms to realize efficient fault tolerant and reconfigurable systems [83,109,110,166]. Neuroplasticity refers to the ability of the brain to adapt and reconfigure during lifetime operation.…”

Section: Autonomous and Robust Systems The Scope Of Applications Resu...mentioning

confidence: 99%

Spiking Neural Networks Hardware Implementations and Challenges

Bouvier

Valentian

Mesquida

et al. 2019

J. Emerg. Technol. Comput. Syst.

179

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Neuromorphic computing is henceforth a major research field for both academic and industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at bringing closer the memory and the computational elements to efficiently evaluate machine-learning algorithms. Recently, Spiking Neural Networks, a generation of cognitive algorithms employing computational primitives mimicking neuron and synapse operational principles, have become an important part of deep learning. They are expected to improve the computational performance and efficiency of neural networks, but are best suited for hardware able to support their temporal dynamics. In this survey, we present the state of the art of hardware implementations of spiking neural networks and the current trends in algorithm elaboration from model selection to training mechanisms. The scope of existing solutions is extensive; we thus present the general framework and study on a case-by-case basis the relevant particularities. We describe the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level and discuss their related advantages and challenges.

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