CAT: Compression-Aware Training for bandwidth reduction

Understand

Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving visual processing tasks.

• One of the major obstacles hindering the ubiquitous use of CNNs for inference is their relatively high memory bandwidth requirements, which can be a main energy consumer and throughput bottleneck in hardware accelerators.
• Accordingly, an efficient feature map compression method can result in substantial performance gains.
• Inspired by quantization-aware training approaches, we propose a compression-aware training (CAT) method that involves training the model in a way that allows better compression of feature maps during inference.