Real-Time Adaptive Physical Sensor Processing with SNN Hardware

被引：0

作者：

Madrenas, Jordi ^{[1
]}

Vallejo-Mancero, Bernardo ^{[1
]}

Oltra-Oltra, Josep Angel ^{[1
]}

Zapata, Mireya ^{[2
]}

Cosp-Vilella, Jordi ^{[1
]}

Calatayud, Robert ^{[1
]}

Moriya, Satoshi ^{[3
]}

Sato, Shigeo ^{[3
]}

机构：

[1] Univ Politecn Cataluna, Dept Elect Engn, Barcelona, Catalunya, Spain

[2] Univ Indoamer, Ctr Invest Mecatron & Sistemas Interact, MIST, Quito, Ecuador

[3] Tohoku Univ, Elect Commun Res Inst, Sendai, Japan

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PT V | 2023年 / 14258卷

关键词：

Spiking Neural Networks (SNNs); HEENS; Real-time sensor processing; Spike-rate filters; adaptive-range sensors; MODEL; NETWORKS; NEURONS;

D O I：

10.1007/978-3-031-44192-9_34

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Spiking Neural Networks (SNNs) offer bioinspired computation based on local adaptation and plasticity as well as close biological compatibility. In this work, after reviewing the Hardware Emulator of Evolving Neural Systems (HEENS) architecture and its Computer-Aided Engineering (CAE) design flow, a spiking implementation of an adaptive physical sensor input scheme based on time-rate Band-Pass Filter (BPF) is proposed for real-time execution of large dynamic range sensory edge processing nodes. Simulation and experimental results of the SNN operating in real-time with an adaptive-range accelerometer input example are shown. This work opens the path to compute with SNNs multiple physical sensor information for perception applications.

引用

页码：423 / 434

页数：12

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