Asynchronous interface circuit for nonlinear connectivity in multicore spiking neural networks

Abstract To expand the scale of spiking neural networks (SNNs), an interface circuit that supports multiple SNN cores is essential. This circuit should be designed using an asynchronous approach to leverage characteristics of SNNs similar to those of the human brain. However, the absence of a global clock presents timing issues during implementation. Hence, we propose an intermediate latching template to establish asynchronous nonlinear connectivity with multipipeline processing between multiple SNN cores. We design arbitration and distribution blocks in the interface circuit based on the proposed template and fabricate an interface circuit that supports four SNN cores using a full‐custom approach in a 28‐nm CMOS (complementary metal–oxide–semiconductor) FDSOI (fully depleted silicon on insulator) process. The proposed template can enhance throughput in the interface circuit by up to 53% compared with the conventional asynchronous template. The interface circuit transmits spikes while consuming 1.7 and 3.7 pJ of power, supporting 606 and 59 Mevent/s in intrachip and interchip communications, respectively.

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Publication Details

Journal
ETRI Journal
Published
2024-10-01
DOI
https://doi.org/10.4218/etrij.2024-0135
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.21

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article

Asynchronous interface circuit for nonlinear connectivity in multicore spiking neural networks

Mi‐Jeong Park, Taewook Kang, Kwang‐Il Oh, Sung‐Eun Kim et al.
1 citations
ETRI Journal
Advanced Memory and Neural Computing
0.21
article

Asynchronous interface circuit for nonlinear connectivity in multicore spiking neural networks

Mi‐Jeong Park, Taewook Kang, Kwang‐Il Oh, Sung‐Eun Kim, Jae‐Jin Lee, Sukho Lee, Hyuk Kim
article en
1 citations

Abstract

Abstract To expand the scale of spiking neural networks (SNNs), an interface circuit that supports multiple SNN cores is essential. This circuit should be designed using an asynchronous approach to leverage characteristics of SNNs similar to those of the human brain. However, the absence of a global clock presents timing issues during implementation. Hence, we propose an intermediate latching template to establish asynchronous nonlinear connectivity with multipipeline processing between multiple SNN cores. We design arbitration and distribution blocks in the interface circuit based on the proposed template and fabricate an interface circuit that supports four SNN cores using a full‐custom approach in a 28‐nm CMOS (complementary metal–oxide–semiconductor) FDSOI (fully depleted silicon on insulator) process. The proposed template can enhance throughput in the interface circuit by up to 53% compared with the conventional asynchronous template. The interface circuit transmits spikes while consuming 1.7 and 3.7 pJ of power, supporting 606 and 59 Mevent/s in intrachip and interchip communications, respectively.

ETRI JournalVol. 46(5)
Electronics and Telecommunications Research Institute (KR)
Electronics and Telecommunications Research Institute
Openalex Percentile: Top 47%
Advanced Memory and Neural Computing
0.21
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Asynchronous interface circuit for nonlinear connectivity in multicore spiking neural networks — Mi‐Jeong Park, Taewook Kang, et al. · ETRI Journal (2024) | TGRS Research Map | TGRS