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A Directed-Evolution Method for Sparsification and Compression of Neural Networks with Application to Object Identification and Segmentation and considerations of optimal quantization using small number of bits
After the desired sparsification level is reached in each layer of the network by DE, a variety of quantization alternatives are used on the surviving parameters to find the lowest number of bits for their representation with acceptable loss of accuracy.
Field-Programmable Deep Neural Network (DNN) Learning and Inference accelerator: a concept
The computational delay per layer is made roughly the same along pipelined accelerator structure.
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