Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays

Deep neural networks are biologically inspired class of algorithms that have recently demonstrated the state-of-the-art accuracy in large-scale classification and recognition tasks. Hardware acceleration of deep networks is of paramount importance to ensure their ubiquitous presence in future comput...

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Veröffentlicht in:	IEEE transactions on circuits and systems. I, Regular papers Jg. 66; H. 8; S. 3064 - 3076
Hauptverfasser:	Agrawal, Amogh, Jaiswal, Akhilesh, Roy, Deboleena, Han, Bing, Srinivasan, Gopalakrishnan, Ankit, Aayush, Roy, Kaushik
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Acceleration Accelerators Accuracy Algorithms Analog to digital conversion Analog to digital converters Arrays Artificial neural networks binary convolution binary neural networks Biological neural networks deep-CNNs Hardware Image classification In-memory computing Machine learning Neural networks Proposals Random access memory SRAM Static random access memory Switches Switching theory Task analysis Transistors Viability
ISSN:	1549-8328, 1558-0806
Online-Zugang:	Volltext
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