id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_lxfkgxtl7za4dgjpbee5atxncy	Yang Yang Lee	Stochastic computing in convolutional neural network implementation: a review	2020	35	.pdf	application/pdf	14279	2367	68	Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic computing whereby a single logic gate can perform the arithmetic operation Keywords Stochastic computing, Convolutional Neural Network, Deep learning, FPGA, IoT conventional binary in this specific use case, driving the rise of stochastic computing (SC). Stochastic computing in convolutional neural network implementation: a review. (2) How exactly is the CNN being computed/executed in the stochastic domain? computed stochastic streams can be converted back to the binary domain by using a simple activated convolution neuron block could perform as accurate as binary computing CNN Figure 19 Process flow in SC BNN, stochastic image generation methodology and the internal computing domain interchange. VLSI implementation of deep neural network using integral stochastic computing. A new stochastic computing methodology for efficient neural network implementation. Accurate and efficient stochastic computing hardware for convolutional neural networks. Neural Network (CNN) accelerators based on stochastic computing.	./cache/work_lxfkgxtl7za4dgjpbee5atxncy.pdf	./txt/work_lxfkgxtl7za4dgjpbee5atxncy.txt