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Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

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EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing.

Jianwei Xue¹, Lisheng Xie¹, Faquan Chen¹

¹School of Electronic and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Sensors (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

EdgeMap optimizes Spiking Neural Networks (SNNs) for edge devices, significantly reducing latency, energy use, and communication costs. This toolchain enhances SNN deployment on neuromorphic hardware for efficient edge computing applications.

Keywords:

edge computing mapping neuromorphic hardware spiking neural networks

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Area of Science:

Artificial Intelligence
Neuromorphic Computing
Edge Computing

Background:

Spiking Neural Networks (SNNs) offer intelligence and energy efficiency for edge computing.
Current SNN mapping methods on neuromorphic hardware suffer from high latency, low throughput, and poor energy/connectivity management.
These limitations hinder the practical deployment of SNNs in edge computing.

Purpose of the Study:

To introduce EdgeMap, an optimized toolchain for deploying SNNs on edge devices.
To overcome the performance and efficiency limitations of existing SNN mapping schemes.
To enable high-performance, energy-efficient SNN applications in edge computing scenarios.

Main Methods:

SNN graph partitioning using a streaming graph partition algorithm, clustering neurons based on hardware constraints.
Multi-objective optimization algorithm for minimizing energy and communication costs during mapping.
Evaluation of EdgeMap across four diverse SNN applications.

Main Results:

EdgeMap reduced average latency by up to 19.8%, energy consumption by 57%, and communication costs by 58%.
Execution time was improved by a factor of 1225.44×.
Throughput was increased by up to 4.02× compared to state-of-the-art methods.

Conclusions:

EdgeMap provides a highly efficient and effective solution for deploying SNNs on edge devices.
The toolchain significantly enhances performance metrics crucial for edge computing.
EdgeMap demonstrates strong utility for real-world SNN applications in resource-constrained edge environments.