C. D. Schuman and S. R. Young and J. P. Mitchell and J. T. Johnston and D. Rose and B. P. Maldonado and B. C. Kaul

October, 2020

IEEE Symposium Series on Computational Intelligence (SSCI)

https://doi.org/10.1109/IGSC51522.2020.9291228

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Abstract

Neuromorphic computing offers one path forward for AI at the edge. However, accessing and effectively utilizing a neuromorphic hardware platform is non-trivial. In this work, we present a complete pipeline for neuromorphic computing at the edge, including a small, inexpensive, low-power, FPGA-based neuromorphic hardware platform, a training algorithm for designing spiking neural networks for neuromorphic hardware, and a software framework for connecting those components. We demonstrate this pipeline on a real-world application, engine control for a spark-ignition internal combustion engine. We illustrate how we connect engine simulations with neuromorphic hardware simulations and training software to produce hardware-compatible spiking neural networks that perform engine control to improve fuel efficiency. We present initial results on the performance of these spiking neural networks and illustrate that they outperform open-loop engine control. We also give size, weight, and power estimates for a deployed solution of this type.

Citation Information

Text

author      C. D. Schuman and S. R. Young and J. P. Mitchell and J. T. Johnston and D. Rose and B. P. Maldonado and B. C. Kaul
title       Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency
booktitle   11th International Green and Sustainable Computing Conference (IGSC)
year        2020
pages       1-8
publisher   IEEE
url         https://doi.org/10.1109/IGSC51522.2020.9291228
doi         10.1109/IGSC51522.2020.9291228

Bibtex

@INPROCEEDINGS{sym:21:rte,
    author = "C. D. Schuman and S. R. Young and J. P. Mitchell and J. T. Johnston and D. Rose and B. P. Maldonado and B. C. Kaul",
    title = "Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency",
    booktitle = "11th International Green and Sustainable Computing Conference (IGSC)",
    year = "2020",
    pages = "1-8",
    organization = "IEEE",
    url = "https://doi.org/10.1109/IGSC51522.2020.9291228",
    doi = "10.1109/IGSC51522.2020.9291228"
}