{"title":"A 0.66-mm<sup>2</sup> 0.49 pJ/SOP SNN Processor with Temporal-Spatial Post-Neuron-Processing and Model-Adaptive Crossbar in 40-nm CMOS.","authors":"Jinqiao Yang, Zikai Zhu, Haoming Chu, Anqin Xiao, Yuxiang Huan, Lirong Zheng, Zhuo Zou","doi":"10.1109/TBCAS.2025.3582246","DOIUrl":null,"url":null,"abstract":"<p><p>This paper presents a Spiking Neural Network (SNN) processor specifically designed to overcome the limitations of existing parallel architectures in maintaining high energy efficiency and model adaptability in a compact area footprint for Artificial Intelligence of Things (AIoT). This is achieved through two key design features: a Temporal-Spatial Post-Neuron Processing (PoNP) scheme that efficiently reuses membrane potential, maximizes parallelism, and reduces memory bank requirements; and a Model-Adaptive Crossbar design with preconfigured parameters and a dynamic switching mechanism enables processing of various SNN models through operation orchestration without efficiency degradation. Using an 8-way parallel pipeline design, the processor achieves a throughput of 128 Synaptic Operations (SOPs) per cycle, resulting in a 2.8× enhancement in energy efficiency. Fabricated in a 40-nm CMOS process, the chip occupies a compact core area of 0.66 mm<sup>2</sup>. It achieves a power consumption of 6.26 mW, an energy efficiency of 0.49 pJ/SOP, and a throughput of 12.8 GSOPS/s at 0.75 V, 100 MHz. The chip is evaluated using typical spatial, temporal, and temporal-spatial datasets, including MIT-BIH, MNIST, N-MNIST, NavGesture, and SHD. These results demonstrate that our chip achieves best-in-class in terms of energy efficiency and latency compared to state-of-the-art architectures.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on biomedical circuits and systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TBCAS.2025.3582246","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents a Spiking Neural Network (SNN) processor specifically designed to overcome the limitations of existing parallel architectures in maintaining high energy efficiency and model adaptability in a compact area footprint for Artificial Intelligence of Things (AIoT). This is achieved through two key design features: a Temporal-Spatial Post-Neuron Processing (PoNP) scheme that efficiently reuses membrane potential, maximizes parallelism, and reduces memory bank requirements; and a Model-Adaptive Crossbar design with preconfigured parameters and a dynamic switching mechanism enables processing of various SNN models through operation orchestration without efficiency degradation. Using an 8-way parallel pipeline design, the processor achieves a throughput of 128 Synaptic Operations (SOPs) per cycle, resulting in a 2.8× enhancement in energy efficiency. Fabricated in a 40-nm CMOS process, the chip occupies a compact core area of 0.66 mm2. It achieves a power consumption of 6.26 mW, an energy efficiency of 0.49 pJ/SOP, and a throughput of 12.8 GSOPS/s at 0.75 V, 100 MHz. The chip is evaluated using typical spatial, temporal, and temporal-spatial datasets, including MIT-BIH, MNIST, N-MNIST, NavGesture, and SHD. These results demonstrate that our chip achieves best-in-class in terms of energy efficiency and latency compared to state-of-the-art architectures.