A Compact 140nW/input Winner-Take-All Circuit for Spiking Neural Networks

Solving classification problems using Spiking Neural Networks (SNNs) involves determining the most active neuron in the output layer. Scalable, low-power and low-area hardware solutions for such decision-making are vital for neuromorphic edge applications to meet power and space constraints. In this...

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Bibliographic Details
Main Authors: R, Gaurav, Kadam, Abhishek A., Singh, Ajay K., Somappa, Laxmeesha, Baghini, Maryam Shojaei, Ganguly, Udayan
Format: Conference Proceeding
Language:English
Subjects:
edge applications
Feedback circuits
Hardware
Image edge detection
Neuromorphics
Neurons
Scalability
Spiking neural networks
Vectors
winner-take-all
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Summary:Solving classification problems using Spiking Neural Networks (SNNs) involves determining the most active neuron in the output layer. Scalable, low-power and low-area hardware solutions for such decision-making are vital for neuromorphic edge applications to meet power and space constraints. In this work, we propose a low-power, compact Winner-Take-All (WTA) circuit, a multi-input multi-output dynamic threshold comparator that simultaneously compares multiple analog voltage inputs and provides a one-hot-encoded digital output vector indicating the result of the classification. The design eliminates the need for cascading and a dedicated feedback circuit. A spike integrator stage captures the temporal activity of a set of neurons, and these activities are compared and digitized by the proposed WTA comparator stage. The proposed WTA designed in GF45RFSOI technology, exhibits self-excitation and global-inhibition properties, offers scalability, consumes 44% less power (140 nW ) and occupies a 40% lower area (166 μm 2 ), compared to state-of-the-art.
ISSN:2158-1525
DOI:10.1109/ISCAS58744.2024.10558381