A 65-nm Neuromorphic Image Classification Processor With Energy-Efficient Training Through Direct Spike-Only Feedback

Recent advances in neural network (NN) and machine learning algorithms have sparked a wide array of research in specialized hardware, ranging from high-performance NN accelerators for use inside the server systems to energy-efficient edge computing systems. While most of these studies have focused o...

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Veröffentlicht in:	IEEE journal of solid-state circuits 2020-01, Vol.55 (1), p.108-119
Hauptverfasser:	Park, Jeongwoo, Lee, Juyun, Jeon, Dongsuk
Format:	Artikel
Sprache:	eng
Schlagworte:	Accelerators Algorithms Artificial intelligence Artificial neural networks Back propagation Cognitive tasks Computational efficiency Design modifications digital integrated circuits Edge computing Electronic devices Energy Energy consumption Handwriting Hardware Image classification Inference learning systems Machine learning Machine learning algorithms Microprocessors multi layer perceptrons Neural networks Neuromorphics Neurons Task analysis Training very large-scale integration
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