Spike-Predictable Neuron Circuits with Adaptive Threshold for Low-Power SNN Systems
Author:
Affiliation:
1. Dongguk University,Department of Semiconductor Science,Seoul,Korea,04620
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10181241/10181318/10181408.pdf?arnumber=10181408
Reference10 articles.
1. A low-power adaptive integrate-and-fire neuron circuit
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3. An 82n W 0.53pJ/SOP Clock-Free Spiking Neural Network with 40?s Latency for AloT Wake-Up Functions Using Ultimate-Event-Driven Bionic Architecture and Computing-in-Memory Technique;liu;IEEE International Solid-State Circuits Conference ISSCC 2022,0
4. Improving spiking neural network accuracy using time-based neurons;hanseok;ArXiv Preprint,2022
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1. Two-step Classification Neuron Circuits for Low-power and High-integration SNN Systems;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19
2. Centerline Extraction of Line Structured Light in 3D Reconstruction;2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE);2023-10-20
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