View in EDS

ModNEF : An Open Source Modular Neuromorphic Emulator for FPGA for Low-Power In-Edge Artificial Intelligence

Saved in:
Bibliographic Details
Title: ModNEF : An Open Source Modular Neuromorphic Emulator for FPGA for Low-Power In-Edge Artificial Intelligence
Authors: Aurélie Saulquin, Mazdak Fatahi, Pierre Boulet, Samy Meftali
Contributors: Saulquin, Aurélie
Source: ACM Transactions on Architecture and Code Optimization. 22:1-24
Publisher Information: Association for Computing Machinery (ACM), 2025.
Publication Year: 2025
Subject Terms: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR], Neuromorphic accelerator, Artificial Intelligence, Spiking Neural Network, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Spiking Neural Network FPGA Neuromorphic accelerator Edge Computing Artificial Intelligence Spiking Neural Network Architecture Parallel Architecture Embbeded Artificial Intelligence, Edge Computing, Embbeded Artificial Intelligence, Parallel Architecture, FPGA, Spiking Neural Network Architecture, [INFO.INFO-ES] Computer Science [cs]/Embedded Systems
Description: Neuromorphic computing is a novel computational paradigm that draws inspiration from the structure and function of the human brain. Spiking Neural Networks (SNNs) are a promising approach for implementing energy-efficient Artificial Neural Networks (ANNs) in embedded systems. In this article, we present ModNEF, an open-source, neuromorphic digital hardware architecture designed for Field Programmable Gate Arrays (FPGAs). ModNEF is based on a modular architecture, where independent modules communicate via point-to-point connections to emulate SNNs. Our architecture offers two neuron models based on the Leaky Integrate and Fire (LIF) model, with a different emulation strategy. The modular nature of ModNEF allows researchers to extend the architecture by developing new modules to emulate different types of neurons or implement online learning rules. ModNEF is a clock-driven emulator, meaning that the neuron state is updated at regular intervals, even in the absence of input data. We evaluated the performance of the emulator using the MNIST and NMNIST datasets, with offline, full-precision training.
Document Type: Article
File Description: application/pdf
Language: English
ISSN: 1544-3973
1544-3566
DOI: 10.1145/3730581
Access URL: https://hal.science/hal-05033844v1/document
https://hal.science/hal-05033844v1
https://doi.org/10.1145/3730581
Rights: CC BY NC
Accession Number: edsair.doi.dedup.....0de931a7c42737d30164f0833cddef3f
Database: OpenAIRE
Description
Abstract:Neuromorphic computing is a novel computational paradigm that draws inspiration from the structure and function of the human brain. Spiking Neural Networks (SNNs) are a promising approach for implementing energy-efficient Artificial Neural Networks (ANNs) in embedded systems. In this article, we present ModNEF, an open-source, neuromorphic digital hardware architecture designed for Field Programmable Gate Arrays (FPGAs). ModNEF is based on a modular architecture, where independent modules communicate via point-to-point connections to emulate SNNs. Our architecture offers two neuron models based on the Leaky Integrate and Fire (LIF) model, with a different emulation strategy. The modular nature of ModNEF allows researchers to extend the architecture by developing new modules to emulate different types of neurons or implement online learning rules. ModNEF is a clock-driven emulator, meaning that the neuron state is updated at regular intervals, even in the absence of input data. We evaluated the performance of the emulator using the MNIST and NMNIST datasets, with offline, full-precision training.
ISSN:15443973
15443566
DOI:10.1145/3730581