  Recently, deep convolutional neural networks (CNNs) have achieved many
eye-catching results. However, deploying CNNs on resource-constrained edge
devices is constrained by limited memory bandwidth for transmitting large
intermediated data during inference, i.e., activation. Existing research
utilizes mixed-precision and dimension reduction to reduce computational
complexity but pays less attention to its application for activation
compression. To further exploit the redundancy in activation, we propose a
learnable mixed-precision and dimension reduction co-design system, which
separates channels into groups and allocates specific compression policies
according to their importance. In addition, the proposed dynamic searching
technique enlarges search space and finds out the optimal bit-width allocation
automatically. Our experimental results show that the proposed methods improve
3.54%/1.27% in accuracy and save 0.18/2.02 bits per value over existing
mixed-precision methods on ResNet18 and MobileNetv2, respectively.
