Abstract:In convolutional neural network (CNN) accelerators, the dominant power consumption is caused by the access of external data memory. In addition, power and area occupied by I/O interfaces maintaining low bit-error-rate, e.g., 1e-15, grow as the data rate increases. Considering the inherent error resilience of the inference process in machine learning applications, the requirement of error-free communication in the data-path is controversial. In this paper, a custom CNN accelerator integrating a channel emulator… Show more
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