A 5-mm², 4.7-μW Convolutional Neural Network Layer Accelerator for Miniature Systems

This brief presents an energy-efficient accelerator for convolutional neural network (CNN) layer computations in a compact system. The accelerator replaces traditional data shift registers with a multiplexer-based barrel shifter, offering greater flexibility for supporting various models and reducing power consumption by 56.2% compared to flip-flop-based shifters. The prototype, fabricated using a 180-nm CMOS process, accelerates CIFAR-10 dataset CNN computations by 8.5 times compared to a system without the accelerator. It achieves this speedup while consuming only $4.7 ~\mu \text{W}$ of power and $9.53 ~\mu \text{J}$ for each inference task.