Ultra-Low-Power Spiking Neurons in 7 nm FinFET Technology: A Comparative Analysis of Leaky Integrate-and-Fire, Morris-Lecar, and Axon-Hillock Architectures
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909602922102784 |
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| author | Larsh, Logan Siddique, Raiyan Banad, Sarah Sharif Yaser Mike |
| author_facet | Larsh, Logan Siddique, Raiyan Banad, Sarah Sharif Yaser Mike |
| contents | Neuromorphic computing aims to replicate the brain's remarkable energy efficiency and parallel processing capabilities for large-scale artificial intelligence applications. In this work, we present a comprehensive comparative study of three spiking neuron circuit architectures-Leaky-Integrate-and-Fire (LIF), Morris-Lecar (ML), and Axon-Hillock (AH)-implemented in a 7 nm FinFET technology. Through extensive SPICE simulations, we explore the optimization of spiking frequency, energy per spike, and static power consumption. Our results show that the AH design achieves the highest throughput, demonstrating multi-gigahertz firing rates (up to 3 GHz) with attojoule energy costs. By contrast, the ML architecture excels in subthreshold to near-threshold regimes, offering robust low-power operation (as low as 0.385 aJ/spike) and biological bursting behavior. Although LIF benefits from a decoupled current mirror for high-frequency operation, it exhibits slightly higher static leakage compared to ML and AH at elevated supply voltages. Comparisons with previous node implementations (22 nm planar, 28 nm) reveal that 7 nm FinFETs can drastically boost energy efficiency and speed albeit at the cost of increased subthreshold leakage in deep subthreshold regions. By quantifying design trade-offs for each neuron architecture, our work provides a roadmap for optimizing spiking neuron circuits in advanced nanoscale technologies to deliver neuromorphic hardware capable of both ultra-low-power operation and high computational throughput. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ultra-Low-Power Spiking Neurons in 7 nm FinFET Technology: A Comparative Analysis of Leaky Integrate-and-Fire, Morris-Lecar, and Axon-Hillock Architectures Larsh, Logan Siddique, Raiyan Banad, Sarah Sharif Yaser Mike Neural and Evolutionary Computing Artificial Intelligence Hardware Architecture Neuromorphic computing aims to replicate the brain's remarkable energy efficiency and parallel processing capabilities for large-scale artificial intelligence applications. In this work, we present a comprehensive comparative study of three spiking neuron circuit architectures-Leaky-Integrate-and-Fire (LIF), Morris-Lecar (ML), and Axon-Hillock (AH)-implemented in a 7 nm FinFET technology. Through extensive SPICE simulations, we explore the optimization of spiking frequency, energy per spike, and static power consumption. Our results show that the AH design achieves the highest throughput, demonstrating multi-gigahertz firing rates (up to 3 GHz) with attojoule energy costs. By contrast, the ML architecture excels in subthreshold to near-threshold regimes, offering robust low-power operation (as low as 0.385 aJ/spike) and biological bursting behavior. Although LIF benefits from a decoupled current mirror for high-frequency operation, it exhibits slightly higher static leakage compared to ML and AH at elevated supply voltages. Comparisons with previous node implementations (22 nm planar, 28 nm) reveal that 7 nm FinFETs can drastically boost energy efficiency and speed albeit at the cost of increased subthreshold leakage in deep subthreshold regions. By quantifying design trade-offs for each neuron architecture, our work provides a roadmap for optimizing spiking neuron circuits in advanced nanoscale technologies to deliver neuromorphic hardware capable of both ultra-low-power operation and high computational throughput. |
| title | Ultra-Low-Power Spiking Neurons in 7 nm FinFET Technology: A Comparative Analysis of Leaky Integrate-and-Fire, Morris-Lecar, and Axon-Hillock Architectures |
| topic | Neural and Evolutionary Computing Artificial Intelligence Hardware Architecture |
| url | https://arxiv.org/abs/2505.03764 |