# Composing Neural Algorithms with Fugu

arXiv: https://arxiv.org/abs/1905.12130 · HTML: https://mlsanity.com/paper/1905.12130/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 6 of 6)

- [NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems](https://mlsanity.com/paper/2304.04640/) (2023)
- [Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware](https://mlsanity.com/paper/2405.01305/) (2024)
- [General-purpose Dataflow Model with Neuromorphic Primitives](https://mlsanity.com/paper/2408.01090/) (2024)
- [Neuromorphic Programming: Emerging Directions for Brain-Inspired Hardware](https://mlsanity.com/paper/2410.22352/) (2024)
- [SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks](https://mlsanity.com/paper/2608.08479/) (2026)
- [Syn2Logic: End-to-End Neuromorphic Design Automation](https://mlsanity.com/paper/2608.25536/) (2026)

## Abstract (arXiv)

Neuromorphic hardware architectures represent a growing family of potential post-Moore's Law Era platforms. Largely due to event-driving processing inspired by the human brain, these computer platforms can offer significant energy benefits compared to traditional von Neumann processors. Unfortunately there still remains considerable difficulty in successfully programming, configuring and deploying neuromorphic systems. We present the Fugu framework as an answer to this need. Rather than necessitating a developer attain intricate knowledge of how to program and exploit spiking neural dynamics to utilize the potential benefits of neuromorphic computing, Fugu is designed to provide a higher level abstraction as a hardware-independent mechanism for linking a variety of scalable spiking neural algorithms from a variety of sources. Individual kernels linked together provide sophisticated processing through compositionality. Fugu is intended to be suitable for a wide-range of neuromorphic applications, including machine learning, scientific computing, and more brain-inspired neural algorithms. Ultimately, we hope the community adopts this and other open standardization attempts allowing for free exchange and easy implementations of the ever-growing list of spiking neural algorithms.