In this chapter, we proposed a low-cost and energy -efficient design for hardware implementation of CNNs. LD deterministic bit -streams and simple standard AND gates are used to perform fast and accurate multiplication operations in the first layer of the NN. Compared to prior random bit -stream -based designs, the proposed design achieves a lower misclassification rate for the same processing time. Evaluating LeNet5 NN with MINIST dataset as the input, the proposed design achieved the same classification rate as the conventional fixed-point binary design with 70% saving in the energy consumption of the first convolutional layer. If accepting slight inaccuracies, higher energy savings are also feasible by processing shorter bit -streams.
Stochastic-binary convolutional neural networks with deterministic bit-streams, Page 1 of 2
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