An Efficient Multicast Addressing Encoding Scheme for Multi-Core Neuromorphic Processors

Multi-Core neuromorphic processors are becoming increasingly significant due to their energy-efficient local computing and scalable modular architecture, particularly for event-based processing applications. However, minimizing the cost of inter-core communication, which accounts for the majority of energy usage, remains a challenging issue. Beyond optimizing circuit design at lower abstraction levels, an efficient multicast addressing scheme is crucial. We propose a hierarchical bit string encoding scheme that largely expands the addressing capability of state-of-the-art symbol-based schemes for the same number of routing bits. When put at work with a real neuromorphic task, this hierarchical bit string encoding achieves a reduction in area cost by approximately 29% and decreases energy consumption by about 50%.

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Publication Details

Published
2025-05-25
DOI
https://doi.org/10.1109/iscas56072.2025.11043591
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

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article

An Efficient Multicast Addressing Encoding Scheme for Multi-Core Neuromorphic Processors

Davide Bertozzi, Giacomo Indiveri, Zhe Su, Aron Bencsik
1 citations
Advanced Memory and Neural Computing
0.73
article

An Efficient Multicast Addressing Encoding Scheme for Multi-Core Neuromorphic Processors

Davide Bertozzi, Giacomo Indiveri, Zhe Su, Aron Bencsik
article en
1 citations

Abstract

Multi-Core neuromorphic processors are becoming increasingly significant due to their energy-efficient local computing and scalable modular architecture, particularly for event-based processing applications. However, minimizing the cost of inter-core communication, which accounts for the majority of energy usage, remains a challenging issue. Beyond optimizing circuit design at lower abstraction levels, an efficient multicast addressing scheme is crucial. We propose a hierarchical bit string encoding scheme that largely expands the addressing capability of state-of-the-art symbol-based schemes for the same number of routing bits. When put at work with a real neuromorphic task, this hierarchical bit string encoding achieves a reduction in area cost by approximately 29% and decreases energy consumption by about 50%.

SIB Swiss Institute of Bioinformatics (CH), University of Zurich (CH), University of Manchester (GB), ETH Zurich (CH)
European Commission
Affordable and clean energy
Openalex Percentile: Top 27%
Advanced Memory and Neural Computing
0.73
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