# Quantization of Deep Neural Networks to facilitate self-correction of weights on Phase Change Memory-based analog hardware

arXiv: https://arxiv.org/abs/2310.00337 · HTML: https://mlsanity.com/paper/2310.00337/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices](https://mlsanity.com/paper/1705.08014/) (2017)
- [A 120dB Programmable-Range On-Chip Pulse Generator for Characterizing Ferroelectric Devices](https://mlsanity.com/paper/2202.04049/) (2022)

## Abstract (arXiv)

In recent years, hardware-accelerated neural networks have gained significant attention for edge computing applications. Among various hardware options, crossbar arrays, offer a promising avenue for efficient storage and manipulation of neural network weights. However, the transition from trained floating-point models to hardware-constrained analog architectures remains a challenge. In this work, we combine a quantization technique specifically designed for such architectures with a novel self-correcting mechanism. By utilizing dual crossbar connections to represent both the positive and negative parts of a single weight, we develop an algorithm to approximate a set of multiplicative weights. These weights, along with their differences, aim to represent the original network's weights with minimal loss in performance. We implement the models using IBM's aihwkit and evaluate their efficacy over time. Our results demonstrate that, when paired with an on-chip pulse generator, our self-correcting neural network performs comparably to those trained with analog-aware algorithms.