HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS

Haihao Shen, Jiong Gong, Xiaoli Liu, Guoming Zhang, Ge Jin, and Eric Lin

Abstract:

High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications. This paper presents a general technique toward 8-bit low precision inference of convolutional neural networks, including 1) channel-wise scale factors of weights, especially for depthwise convolution, 2) Winograd convolution, and 3) topology-wise 8-bit support. We experiment the techniques on top of a widely-used deep learning framework. The 8-bit optimized model is automatically generated with a calibration process from FP32 model without the need of fine-tuning or retraining. We perform a systematical and comprehensive study on 18 widely-used convolutional neural networks and demonstrate the effectiveness of 8-bit low precision inference across a wide range of applications and use cases, including image classification, object detection, image segmentation, and super resolution. We show that the inference throughput
and latency are improved by 1.6X and 1.5X respectively with minimal within 0.6%1to no loss in accuracy from FP32 baseline. We believe the methodology can provide the guidance and reference design of 8-bit low precision inference for other frameworks. All the code and models will be publicly available soon.

Keywords: 8-bit low precision inference, convolutional neural networks, statistical accuracy, 8-bit Winograd convolution

TL;DR: We present a general technique toward 8-bit low precision inference of convolutional neural networks.

Data: [ssd](https://paperswithcode.com/dataset/openlane-v1)

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/highly-efficient-8-bit-low-precision/code)