Towards Exact Gradient-based Training on Analog In-memory Computing
<|reference_start|>Towards Exact Gradient-based Training on Analog In-memory Computing: Given the high economic and environmental costs of using large vision or language models, analog in-memory accelerators present a promising solution for energy-efficient AI. While inference on analog accelerators has been studied recently, the training perspective is underexplored. Recent studies have shown that the "workhorse" of digital AI training - stochastic gradient descent (SGD) algorithm converges inexactly when applied to model training on non-ideal devices. This paper puts forth a theoretical foundation for gradient-based training on analog devices. We begin by characterizing the non-convergent issue of SGD, which is caused by the asymmetric updates on the analog devices. We then provide a lower bound of the asymptotic error to show that there is a fundamental performance limit of SGD-based analog training rather than an artifact of our analysis. To address this issue, we study a heuristic analog algorithm called Tiki-Taka that has recently exhibited superior empirical performance compared to SGD and rigorously show its ability to exactly converge to a critical point and hence eliminates the asymptotic error. The simulations verify the correctness of the analyses.<|reference_end|>
@article{wu2024towards,
    title={Towards Exact Gradient-based Training on Analog In-memory Computing},
    author={Zhaoxian Wu and Tayfun Gokmen and Malte J. Rasch and Tianyi Chen},
    journal={arXiv preprint arXiv:2406.12774},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12774},
    primaryClass={cs.LG cs.AR math.OC}
}