  Convolutional neural networks (CNNs) achieve state-of-the-art accuracy in a
variety of tasks in computer vision and beyond. One of the major obstacles
hindering the ubiquitous use of CNNs for inference on low-power edge devices is
their high computational complexity and memory bandwidth requirements. The
latter often dominates the energy footprint on modern hardware. In this paper,
we introduce a lossy transform coding approach, inspired by image and video
compression, designed to reduce the memory bandwidth due to the storage of
intermediate activation calculation results. Our method does not require
fine-tuning the network weights and halves the data transfer volumes to the
main memory by compressing feature maps, which are highly correlated, with
variable length coding. Our method outperform previous approach in term of the
number of bits per value with minor accuracy degradation on ResNet-34 and
MobileNetV2. We analyze the performance of our approach on a variety of CNN
architectures and demonstrate that FPGA implementation of ResNet-18 with our
approach results in a reduction of around 40% in the memory energy footprint,
compared to quantized network, with negligible impact on accuracy. When
allowing accuracy degradation of up to 2%, the reduction of 60% is achieved. A
reference implementation is available at
https://github.com/CompressTeam/TransformCodingInference

Computer Vision and Pattern Recognition;Machine Learning