TY - GEN
T1 - End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence
AU - Skatchkovsky, Nicolas
AU - Jang, Hyeryung
AU - Simeone, Osvaldo
N1 - Funding Information:
The authors are with King’s College Communication, Learning and Information Processing lab, Centre for TelecommunicationsResearch, Department of Engineering, King’s College London, UK. The authors have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 725731).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - This paper introduces a novel all-spike low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (SNNs). In the proposed system, event-driven neuromorphic sensors produce asynchronous time-encoded data streams that are encoded by an SNN, whose output spiking signals are pulse modulated via IR and transmitted over general frequence-selective channels; while the receiver's inputs are obtained via hard detection of the received signals and fed to an SNN for classification. We introduce an end-to-end training procedure that treats the cascade of encoder, channel, and decoder as a probabilistic SNN-based autoencoder that implements Joint Source-Channel Coding (JSCC). The proposed system, termed NeuroJSCC, is compared to conventional synchronous frame-based and uncoded transmissions in terms of latency and accuracy. The experiments confirm that the proposed end-to-end neuromorphic edge architecture provides a promising framework for efficient and low-latency remote sensing, communication, and inference.
AB - This paper introduces a novel all-spike low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (SNNs). In the proposed system, event-driven neuromorphic sensors produce asynchronous time-encoded data streams that are encoded by an SNN, whose output spiking signals are pulse modulated via IR and transmitted over general frequence-selective channels; while the receiver's inputs are obtained via hard detection of the received signals and fed to an SNN for classification. We introduce an end-to-end training procedure that treats the cascade of encoder, channel, and decoder as a probabilistic SNN-based autoencoder that implements Joint Source-Channel Coding (JSCC). The proposed system, termed NeuroJSCC, is compared to conventional synchronous frame-based and uncoded transmissions in terms of latency and accuracy. The experiments confirm that the proposed end-to-end neuromorphic edge architecture provides a promising framework for efficient and low-latency remote sensing, communication, and inference.
KW - IoT
KW - Neuromorphic learning
KW - Spiking Neural Networks
KW - Wireless Communications
UR - http://www.scopus.com/inward/record.url?scp=85107796406&partnerID=8YFLogxK
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U2 - 10.1109/IEEECONF51394.2020.9443351
DO - 10.1109/IEEECONF51394.2020.9443351
M3 - Conference contribution
AN - SCOPUS:85107796406
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 166
EP - 173
BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Y2 - 1 November 2020 through 5 November 2020
ER -