Genetic Motifs as a Blueprint for Mismatch-Tolerant Neuromorphic Computing
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Date
2024-10-25
Publication Type
Working Paper
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Abstract
Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device mismatch in the analog circuits of these neuromorphic processors poses a significant challenge to the deployment of robust processing in these systems. Here we introduce a novel architectural solution inspired by biological development to address this issue. Specifically we propose to implement architectures that incorporate network motifs found in developed brains through a differentiable re-parameterization of weight matrices based on gene expression patterns and genetic rules. Thanks to the gradient descent optimization compatibility of the method proposed, we can apply the robustness of biological neural development to neuromorphic computing.
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published
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Pages / Article No.
2410.19403
Publisher
Cornell University
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Edition / version
v1
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Subject
Spiking neural networks; Mixed-signal chips; Device mismatch; Network neuroscience
Organisational unit
09699 - Indiveri, Giacomo / Indiveri, Giacomo
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