Implementing Efficient Balanced Networks with Mixed-Signal Spike-Based Learning Circuits
| Autor: | Giacomo Indiveri, Michel Perez, Julian Büchel, Jonathan Kakon |
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| Přispěvatelé: | University of Zurich, Buchel, Julian |
| Jazyk: | angličtina |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Science - Artificial Intelligence Computer Science - Emerging Technologies Cloud computing Integrated circuit law.invention 03 medical and health sciences 0302 clinical medicine Computer Science::Emerging Technologies law Encoding (memory) System on a chip 030304 developmental biology Electronic circuit 10194 Institute of Neuroinformatics 0303 health sciences Quantitative Biology::Neurons and Cognition business.industry 2208 Electrical and Electronic Engineering Mixed-signal integrated circuit Emerging Technologies (cs.ET) Artificial Intelligence (cs.AI) Neuromorphic engineering Computer engineering 570 Life sciences biology Spike (software development) business 030217 neurology & neurosurgery |
| Zdroj: | 2021 IEEE International Symposium on Circuits and Systems (ISCAS) ISCAS |
| DOI: | 10.1109/iscas51556.2021.9401767 |
| Popis: | Efficient Balanced Networks (EBNs) are networks of spiking neurons in which excitatory and inhibitory synaptic currents are balanced on a short timescale, leading to desirable coding properties such as high encoding precision, low firing rates, and distributed information representation. It is for these benefits that it would be desirable to implement such networks in low-power neuromorphic processors. However, the degree of device mismatch in analog mixed-signal neuromorphic circuits renders the use of pre-trained EBNs challenging, if not impossible. To overcome this issue, we developed a novel local learning rule suitable for on-chip implementation that drives a randomly connected network of spiking neurons into a tightly balanced regime. Here we present the integrated circuits that implement this rule and demonstrate their expected behaviour in low-level circuit simulations. Our proposed method paves the way towards a system-level implementation of tightly balanced networks on analog mixed-signal neuromorphic hardware. Thanks to their coding properties and sparse activity, neuromorphic electronic EBNs will be ideally suited for extreme-edge computing applications that require low-latency, ultra-low power consumption and which cannot rely on cloud computing for data processing. 5 pages, 6 figures. Accepted at IEEE International Symposium on Circuits and Systems 2021 |
| Databáze: | OpenAIRE |
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