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InSight: An FPGA-Based Neuromorphic Computing System for Deep Neural Networks
2020
Journal of Low Power Electronics and Applications
Deep neural networks have demonstrated impressive results in various cognitive tasks such as object detection and image classification. This paper describes a neuromorphic computing system that is designed from the ground up for energy-efficient evaluation of deep neural networks. The computing system consists of a non-conventional compiler, a neuromorphic hardware architecture, and a space-efficient microarchitecture that leverages existing integrated circuit design methodologies. The compiler
doi:10.3390/jlpea10040036
fatcat:qgl2htptxrfdpoyuufsnqm6obq