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.

翻译：本文的研究表明，智能处理单元（IPU）可作为GPU在材料科学与电池研究领域机器学习（ML）应用中的可行替代加速器。我们探究了将模型从GPU迁移至IPU的过程，并探索了多种优化技术（包括流水线化与梯度累积），旨在提升基于IPU的模型性能。此外，我们已成功将一项专用模型迁移至IPU平台。该模型用于预测有效电导率——这是离子传输过程中至关重要的参数，而离子传输过程决定了电池多次充放电循环的性能表现。模型采用卷积神经网络（CNN）架构执行有效电导率的预测任务。实验表明，该模型在IPU上的性能与其在GPU上的执行效果相当。我们还分析了Graphcore Bow IPU的利用率与性能。通过基准测试，我们发现Colossus IPU的前代产品相比，Bow IPU展现出显著的性能提升。