The discoveries in this paper show that Intelligence Processing Units (IPUs)
offer a viable accelerator alternative to GPUs for machine learning (ML)
applications within the fields of materials science and battery research. We
investigate the process of migrating a model from GPU to IPU and explore
several optimization techniques, including pipelining and gradient
accumulation, aimed at enhancing the performance of IPU-based models.
Furthermore, we have effectively migrated a specialized model to the IPU
platform. This model is employed for predicting effective conductivity, a
parameter crucial in ion transport processes, which govern the performance of
multiple charge and discharge cycles of batteries. The model utilizes a
Convolutional Neural Network (CNN) architecture to perform prediction tasks for
effective conductivity. The performance of this model on the IPU is found to be
comparable to its execution on GPUs. We also analyze the utilization and
performance of Graphcore's Bow IPU. Through benchmark tests, we observe
significantly improved performance with the Bow IPU when compared to its
predecessor, the Colossus IPU.

通过探索 Intelligence Processing Units (IPUs) 的优化技巧以及将专用模型迁移到 IPU 平台，本研究表明 IPU 是机器学习、材料科学和电池研究领域中 GPU 的一种可行加速器替代方案。在多次充放电循环中，本研究还展示了 IPU 上进行有效电导率预测任务时使用卷积神经网络 (CNN) 架构模型与 GPU 执行相当的性能，并通过基准测试发现 Graphcore 的 Bow IPU 相较于 Colossus IPU 有显著的性能改善。