High Performance Computing (HPC) benefits from different improvements during
last decades, specially in terms of hardware platforms to provide more
processing power while maintaining the power consumption at a reasonable level.
The Intelligence Processing Unit (IPU) is a new type of massively parallel
processor, designed to speedup parallel computations with huge number of
processing cores and on-chip memory components connected with high-speed
fabrics. IPUs mainly target machine learning applications, however, due to the
architectural differences between GPUs and IPUs, especially significantly less
memory capacity on an IPU, methods for reducing model size by sparsification
have to be considered. Butterfly factorizations are well-known replacements for
fully-connected and convolutional layers. In this paper, we examine how
butterfly structures can be implemented on an IPU and study their behavior and
performance compared to a GPU. Experimental results indicate that these methods
can provide 98.5% compression ratio to decrease the immense need for memory,
the IPU implementation can benefit from 1.3x and 1.6x performance improvement
for butterfly and pixelated butterfly, respectively. We also reach to 1.62x
training time speedup on a real-word dataset such as CIFAR10.

高性能计算从硬件平台的不断改进中受益，在保持合理功耗的同时提供更多的处理能力。智能处理单元（IPU）是一种新型的大规模并行处理器，旨在加速具有大量处理核心和高速内存组件的并行计算。本文研究了如何在 IPU 上实现蝶形结构，并研究了它们与 GPU 的行为和性能的比较。实验结果表明，这些方法可以提供 98.5% 的压缩比，减少对内存的极度需求。在蝶形和像素化蝶形方面，IPU 的实现可以获得 1.3 倍和 1.6 倍的性能改进。我们还在 CIFAR10 等实际数据集上实现了 1.62 倍的训练时间加速。