Exploring Spike Encoder Designs for Near-Sensor Edge Computing

Robust sensing and detection require energy- and cost-efficient hardware and software capable of operating reliably in dynamic environments with wide variations in operating conditions. Spiking Neural Networks (SNNs), widely recognized as biologically inspired computing models, offer significant pot...

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Veröffentlicht in:2025 Neuro Inspired Computational Elements (NICE) S. 1 - 9
Hauptverfasser: Jin, Jingang, Zhang, Zhenhang, Qiu, Qinru
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 25.03.2025
Schlagworte:
Adaptation models
Complexity theory
Computational modeling
Computer architecture
Costs
Edge computing
Encoding
Hardware
in-Hardware Learning
Reservoirs
SNN
Spike encoder
Transforms
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Zusammenfassung:Robust sensing and detection require energy- and cost-efficient hardware and software capable of operating reliably in dynamic environments with wide variations in operating conditions. Spiking Neural Networks (SNNs), widely recognized as biologically inspired computing models, offer significant potential for near-sensor signal processing due to their energy efficiency and adaptability. A critical step toward broader adoption of this novel computing paradigm is the development of efficient frontend designs capable of encoding multichannel time-series data from sensors into sparse spike trains. This work introduces two spike-encoder architectures: a population coding-based encoder and a reservoir computing- based encoder. These architectures convert multivariate time series into multichannel spike sequences, performing sparse coding that effectively projects the input temporal sequences into a high-dimensional binary feature space in both spatial and temporal domains. When combined with an SNN-based backend classifier, the encoded spike sequences enable effective classification. Furthermore, our proposed reservoir encoder achieves lower implementation complexity compared to conventional reservoir models while maintaining effective sparse coding capabilities. Finally, we demonstrate that the in- hardware online learning capability of SNN models can alleviate stringent requirements on encoder performance and precision to allow cost reduction and design simplification.
DOI:10.1109/NICE65350.2025.11065206