Special Topic on Energy-Efficient Compute-in-Memory With Emerging Devices

Deep neural networks (DNNs) have shown extraordinary performance in recent years for various applications including image classification, object detection, speech recognition, natural language processing, etc. Accuracydriven DNN architectures tend to increase the model sizes and computations at a very fast pace, demanding massive amount of hardware resources. Frequent communication between processing engine ON-/OFF-chip memory leads high energy consumption, which becomes bottleneck conventional accelerator design.