# Composing Neural Algorithms with Fugu

## TL;DR

Fugu is a framework that simplifies programming neuromorphic hardware by providing a hardware-independent, compositional approach to linking scalable spiking neural algorithms, facilitating diverse applications and community adoption.

## Contribution

It introduces a high-level, hardware-agnostic framework for composing neural algorithms on neuromorphic systems, addressing programming and deployment challenges.

## Key findings

- Enables scalable composition of neural algorithms
- Supports diverse neuromorphic applications
- Promotes community adoption of standard interfaces

## Abstract

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.