Band to Band Tunneling-Based Low Power and Low Area Tunable Spike Delay Element

Bio-inspired axonal and dendritic delay-based spiking neural network algorithms perform spatiotemporal pattern recognition efficiently within feed-forward networks, making complex and suboptimal recurrent neural network structures unnecessary. Including trainable dendritic or axonal delays in feed-forward neural networks reduces neural network complexity and improves classification performance significantly. However, generating tunable low-power hardware spike delays of the biological timescale (few μs to ms ) without adding an extra penalty on the area has been challenging over the years. We present a novel band-to-band tunneling-based tunable delay element for spiking neural network hardware. The proposed low-power core delay element capable of providing spike delays of up to 0.4 ms (without explicit capacitance) consumes an area of 50 μm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in GF45RFSOI technology with a peak power of 320 nW, which is the lowest among state-of-the-art spike delay generation circuits. Moreover, the order of the spike delay can be extended to 25 ms by adding an explicit on-chip capacitance of 500 fF.

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Published
2025-05-25
DOI
https://doi.org/10.1109/iscas56072.2025.11044300
Citations
1
Primary Topic
Advanced Antenna and Metasurface Technologies
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article
Field-Weighted Citation Impact
4.99
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article

Band to Band Tunneling-Based Low Power and Low Area Tunable Spike Delay Element

Laxmeesha Somappa, Maryam Shojaei Baghini, Shreyas Deshmukh, Udayan Ganguly et al.
1 citations
Advanced Antenna and Metasurface Technologies
4.99
article

Band to Band Tunneling-Based Low Power and Low Area Tunable Spike Delay Element

Laxmeesha Somappa, Maryam Shojaei Baghini, Shreyas Deshmukh, Udayan Ganguly, Abhishek Kadam
article en
1 citations

Abstract

Bio-inspired axonal and dendritic delay-based spiking neural network algorithms perform spatiotemporal pattern recognition efficiently within feed-forward networks, making complex and suboptimal recurrent neural network structures unnecessary. Including trainable dendritic or axonal delays in feed-forward neural networks reduces neural network complexity and improves classification performance significantly. However, generating tunable low-power hardware spike delays of the biological timescale (few μs to ms ) without adding an extra penalty on the area has been challenging over the years. We present a novel band-to-band tunneling-based tunable delay element for spiking neural network hardware. The proposed low-power core delay element capable of providing spike delays of up to 0.4 ms (without explicit capacitance) consumes an area of 50 μm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in GF45RFSOI technology with a peak power of 320 nW, which is the lowest among state-of-the-art spike delay generation circuits. Moreover, the order of the spike delay can be extended to 25 ms by adding an explicit on-chip capacitance of 500 fF.

Indian Institute of Technology Bombay (IN)
Affordable and clean energy
Openalex Percentile: Top 6%
Advanced Antenna and Metasurface Technologies
4.99
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Band to Band Tunneling-Based Low Power and Low Area Tunable Spike Delay Element — Laxmeesha Somappa, Maryam Shojaei Baghini, et al. · (2025) | TGRS Research Map | TGRS