A Circuit Concept for Energy-Efficient Spiking Neural Network Systems with a FOM of 86.9fJ/SOP

This paper shows a circuit concept for low-energy neuromorphic hardware for Spiking Neural Networks (SNNs). Combining continuous time-encoded values with time-discrete circuitry helps to improve energy-efficiency. Simple design blocks for neurons and synapses make a scalable neural network design pr...

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Bibliographic Details
Published inBiomedical Circuits and Systems Conference pp. 1 - 5
Main Authors Ochs, Matthias, Dietl, Markus, Brederlow, Ralf
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.10.2024
Subjects
Analog
Biological neural networks
Circuits and systems
Design automation
Energy consumption
Energy efficiency
Hardware
Neuromorphic
Neuromorphics
Neurons
Silicon
SNN
Spiking neural networks
Synapses
Time-discrete
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ISSN2766-4465
DOI10.1109/BioCAS61083.2024.10798224

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Summary:This paper shows a circuit concept for low-energy neuromorphic hardware for Spiking Neural Networks (SNNs). Combining continuous time-encoded values with time-discrete circuitry helps to improve energy-efficiency. Simple design blocks for neurons and synapses make a scalable neural network design process possible. The direct connection between neurons and synapses in an analog manner reduces latency and energy consumption. The functionality was proven by a small neural net solving a classification problem on silicon. Measurements show a FOM of 86.9 fJ for one synaptic operation.
ISSN:2766-4465
DOI:10.1109/BioCAS61083.2024.10798224