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.

翻译：近年来，硬件加速神经网络在边缘计算应用中受到广泛关注。在各类硬件方案中，交叉阵列为神经网络权重的高效存储与运算提供了具有前景的实现路径。然而，从训练完成的浮点模型向硬件受限的模拟架构迁移仍面临挑战。本研究将专为此类架构设计的量化技术与新型自校正机制相结合：通过利用双交叉阵列连接分别表征单个权重的正负分量，我们开发出逼近一组乘法权重的算法。这些权重及其差值旨在以最小性能损失表征原始网络权重。基于IBM aihwkit实现的模型评估结果表明，当配合片上脉冲发生器使用时，本研究所提出的自校正神经网络性能可与基于模拟感知算法训练的模型相媲美。