A 1Mb mixed-precision quantized encoder for image classification and patch-based compression

TL;DR

This paper presents a compact, mixed-precision quantized encoder for image classification and compression, demonstrating high accuracy and effective patch-based image compression with minimal hardware requirements.

Contribution

Introduces a reconfigurable mixed-precision encoder with adaptive quantization and normalization techniques for multi-task image processing on limited hardware.

Findings

Achieves 87.5% accuracy on CIFAR-10 with only 1Mb hardware.

Enables effective patch-based image compression with minimal artifacts.

Outperforms state-of-the-art patch compression techniques.

Abstract

Even if Application-Specific Integrated Circuits (ASIC) have proven to be a relevant choice for integrating inference at the edge, they are often limited in terms of applicability. In this paper, we demonstrate that an ASIC neural network accelerator dedicated to image processing can be applied to multiple tasks of different levels: image classification and compression, while requiring a very limited hardware. The key component is a reconfigurable, mixed-precision (3b/2b/1b) encoder that takes advantage of proper weight and activation quantizations combined with convolutional layer structural pruning to lower hardware-related constraints (memory and computing). We introduce an automatic adaptation of linear symmetric quantizer scaling factors to perform quantized levels equalization, aiming at stabilizing quinary and ternary weights training. In addition, a proposed layer-shared Bit-Shift Normalization significantly simplifies the implementation of the hardware-expensive Batch Normalization. For a specific configuration in which the encoder design only requires 1Mb, the classification accuracy reaches 87.5% on CIFAR-10. Besides, we also show that this quantized encoder can be used to compress image patch-by-patch while the reconstruction can performed remotely, by a dedicated full-frame decoder. This solution typically enables an end-to-end compression almost without any block artifacts, outperforming patch-based state-of-the-art techniques employing a patch-constant bitrate.