# Tricking AI chips into Simulating the Human Brain: A Detailed Performance Analysis

arXiv: https://arxiv.org/abs/2301.13637 · HTML: https://mlsanity.com/paper/2301.13637/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [Dissecting the Graphcore IPU Architecture via Microbenchmarking](https://mlsanity.com/paper/1912.03413/) (2019)
- [BindsNET: A machine learning-oriented spiking neural networks library in Python](https://mlsanity.com/paper/1806.01423/) (2018)
- [NengoDL: Combining deep learning and neuromorphic modelling methods](https://mlsanity.com/paper/1805.11144/) (2018)
- [Efficient Hardware Acceleration of Sparsely Active Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2203.12437/) (2022)
- [Intelligence Processing Units Accelerate Neuromorphic Learning](https://mlsanity.com/paper/2211.10725/) (2022)
- [Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures](https://mlsanity.com/paper/1901.07454/) (2019)
- [CoreNEURON : An Optimized Compute Engine for the NEURON Simulator](https://mlsanity.com/paper/1901.10975/) (2019)
- [EDEN: A high-performance, general-purpose, NeuroML-based neural simulator](https://mlsanity.com/paper/2106.06752/) (2021)

## Built on by (most cited first, 1 of 1)

- [Harnessing Manycore Processors with Distributed Memory for Accelerated Training of Sparse and Recurrent Models](https://mlsanity.com/paper/2311.04386/) (2023)

## Abstract (arXiv)

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.