Name
Title | ||
|---|---|---|
Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGSC51522.2020.9291228 | 2020 11th International Green and Sustainable Computing Workshops (IGSC) |
Keywords | DocType | ISBN |
neuromorphic,FPGA,engine control unit,internal combustion engine | Conference | 978-1-6654-1553-8 |
Citations | PageRank | References |
1 | 0.39 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Catherine D. Schuman | 1 | 5 | 3.59 |
| Steven Young | 2 | 75 | 12.40 |
| J. Parker Mitchell | 3 | 1 | 3.77 |
| J. Travis Johnston | 4 | 3 | 1.79 |
| Derek C. Rose | 5 | 1 | 0.39 |
| Bryan P. Maldonado | 6 | 3 | 2.61 |
| Brian C. Kaul | 7 | 1 | 0.39 |