欺骗AI芯片模拟人脑：详细的性能分析
Tricking AI chips into Simulating the Human Brain: A Detailed Performance Analysis
摘要：
为了打破Nvidia的垄断，专用的AI加速器芯片已经开始出现，用于解决推理，特别是现代深度神经网络（DNN）的训练对现代计算机构成的计算挑战。该领域一直充斥着评估这些参赛者在各种DNN模型类型中表现的研究。然而，人工智能专家意识到当前DNN的局限性，并一直致力于第四次人工智能浪潮，可以说，这将依赖于更多的生物启发模型，主要是脉冲神经网络（SNN）。与此同时，GPU在计算神经科学领域被大量用于模拟此类模型，但AI芯片尚未在此类工作负载上进行测试。本文旨在通过评估多个尖端AI芯片（Graphcore IPU，GroqChip，Nvidia GPU with Tensor Cores和Google TPU）来填补这一重要空白，模拟大脑区域的高度生物详细模型，下橄榄（IO）。此IO应用程序对不同的AI平台进行压力测试，通过改变其计算密度，内存要求和浮点数值精度来突出架构权衡。我们的性能分析表明，模拟问题非常好地映射到GPU和TPU架构上，对于125，000个单元的网络，CPU运行时的加速分别为28倍和1，208倍。在这个速度下，TPU创造了最大实时IO模拟的新纪录。GroqChip在小型网络中的表现优于这两个平台，但由于实现了一些精度较低的浮点运算，因此还不能用于大脑模拟。
英文摘要：
Challenging the Nvidia monopoly, dedicated AI-accelerator chips have begun emerging for tackling the computational challenge that the inference and, especially, the training of modern deep neural networks (DNNs) poses to modern computers. The field has been ridden with studies assessing the performance of these contestants across various DNN model types. However, AI-experts are aware of the limitations of current DNNs and have been working towards the fourth AI wave which will, arguably, rely on more biologically inspired models, predominantly on spiking neural networks (SNNs). At the same time, GPUs have been heavily used for simulating such models in the field of computational neuroscience, yet AI-chips have not been tested on such workloads. The current paper aims at filling this important gap by evaluating multiple, cutting-edge AI-chips (Graphcore IPU, GroqChip, Nvidia GPU with Tensor Cores and Google TPU) on simulating a highly biologically detailed model of a brain region, the inferior olive (IO). This IO application stress-tests the different AI-platforms for highlighting architectural tradeoffs by varying its compute density, memory requirements and floating-point numerical accuracy. Our performance analysis reveals that the simulation problem maps extremely well onto the GPU and TPU architectures, which for networks of 125,000 cells leads to a 28x respectively 1,208x speedup over CPU runtimes. At this speed, the TPU sets a new record for largest real-time IO simulation. The GroqChip outperforms both platforms for small networks but, due to implementing some floating-point operations at reduced accuracy, is found not yet usable for brain simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
