# On the Quantization of Cellular Neural Networks for Cyber-Physical Systems

## TL;DR

This paper introduces a powers-of-two based incremental quantization method for Cellular Neural Networks (CeNNs) to enhance processing efficiency in cyber-physical systems like telemedicine and ADAS, achieving significant speedups without performance loss.

## Contribution

It proposes a novel incremental quantization approach with five strategies for efficient hardware implementation of CeNNs in CPS applications.

## Key findings

- Achieves up to 7.8x speedup on FPGA implementations.
- No performance loss compared to state-of-the-art solutions.
- Introduces five quantization strategies for CeNNs.

## Abstract

Cyber-Physical Systems (CPSs) have been pervasive including smart grid, autonomous automobile systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics. As usually implemented on embedded devices, CPS is typically constrained by computation capacity and energy consumption. In some CPS applications such as telemedicine and advanced driving assistance system (ADAS), data processing on the embedded devices is preferred due to security/safety and real-time requirement. Therefore, high efficiency is highly desirable for such CPS applications. In this paper we present CeNN quantization for high-efficient processing for CPS applications, particularly telemedicine and ADAS applications. We systematically put forward powers-of-two based incremental quantization of CeNNs for efficient hardware implementation. The incremental quantization contains iterative procedures including parameter partition, parameter quantization, and re-training. We propose five different strategies including random strategy, pruning inspired strategy, weighted pruning inspired strategy, nearest neighbor strategy, and weighted nearest neighbor strategy. Experimental results show that our approach can achieve a speedup up to 7.8x with no performance loss compared with the state-of-the-art FPGA solutions for CeNNs.