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