# StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs

arXiv: https://arxiv.org/abs/2106.05373 · HTML: https://mlsanity.com/paper/2106.05373/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [Learning representations in Bayesian Confidence Propagation neural networks](https://mlsanity.com/paper/2003.12415/) (2020)
- [Brain-like approaches to unsupervised learning of hidden representations -- a comparative study](https://mlsanity.com/paper/2005.03476/) (2020)
- [eBrainII: A 3 kW Realtime Custom 3D DRAM integrated ASIC implementation of a Biologically Plausible Model of a Human Scale Cortex](https://mlsanity.com/paper/1911.00889/) (2019)
- [High-Performance High-Order Stencil Computation on FPGAs Using OpenCL](https://mlsanity.com/paper/2002.05983/) (2020)

## Built on by (most cited first, 5 of 5)

- [A domain-specific language for describing machine learning datasets](https://mlsanity.com/paper/2207.02848/) (2022)
- [Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference](https://mlsanity.com/paper/2506.18530/) (2025)
- [Higgs Boson Classification: Brain-inspired BCPNN Learning with StreamBrain](https://mlsanity.com/paper/2107.06676/) (2021)
- [The μG Language for Programming Graph Neural Networks](https://mlsanity.com/paper/2407.09441/) (2024)
- [Native Explainability for Bayesian Confidence Propagation Neural Networks: A Framework for Trusted Brain-Like AI](https://mlsanity.com/paper/2605.11595/) (2026)

## Abstract (arXiv)

The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas. At the same time, there are other -- less-known -- machine learning algorithms with a mature and solid theoretical foundation whose performance remains unexplored. One such example is the brain-like Bayesian Confidence Propagation Neural Network (BCPNN). In this paper, we introduce StreamBrain -- a framework that allows neural networks based on BCPNN to be practically deployed in High-Performance Computing systems. StreamBrain is a domain-specific language (DSL), similar in concept to existing machine learning (ML) frameworks, and supports backends for CPUs, GPUs, and even FPGAs. We empirically demonstrate that StreamBrain can train the well-known ML benchmark dataset MNIST within seconds, and we are the first to demonstrate BCPNN on STL-10 size networks. We also show how StreamBrain can be used to train with custom floating-point formats and illustrate the impact of using different bfloat variations on BCPNN using FPGAs.