EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909230665039872 |
|---|---|
| author | Dobrita, Alexandra Yousefzadeh, Amirreza Thorpe, Simon Vadivel, Kanishkan Detterer, Paul Tang, Guangzhi van Schaik, Gert-Jan Konijnenburg, Mario Gebregiorgis, Anteneh Hamdioui, Said Sifalakis, Manolis |
| author_facet | Dobrita, Alexandra Yousefzadeh, Amirreza Thorpe, Simon Vadivel, Kanishkan Detterer, Paul Tang, Guangzhi van Schaik, Gert-Jan Konijnenburg, Mario Gebregiorgis, Anteneh Hamdioui, Said Sifalakis, Manolis |
| contents | For Edge AI applications, deploying online learning and adaptation on resource-constrained embedded devices can deal with fast sensor-generated streams of data in changing environments. However, since maintaining low-latency and power-efficient inference is paramount at the Edge, online learning and adaptation on the device should impose minimal additional overhead for inference. With this goal in mind, we explore energy-efficient learning and adaptation on-device for streaming-data Edge AI applications using Spiking Neural Networks (SNNs), which follow the principles of brain-inspired computing, such as high-parallelism, neuron co-located memory and compute, and event-driven processing. We propose EON-1, a brain-inspired processor for near-sensor extreme edge online feature extraction, that integrates a fast online learning and adaptation algorithm. We report results of only 1% energy overhead for learning, by far the lowest overhead when compared to other SoTA solutions, while attaining comparable inference accuracy. Furthermore, we demonstrate that EON-1 is up for the challenge of low-latency processing of HD and UHD streaming video in real-time, with learning enabled. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17285 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction Dobrita, Alexandra Yousefzadeh, Amirreza Thorpe, Simon Vadivel, Kanishkan Detterer, Paul Tang, Guangzhi van Schaik, Gert-Jan Konijnenburg, Mario Gebregiorgis, Anteneh Hamdioui, Said Sifalakis, Manolis Neural and Evolutionary Computing Artificial Intelligence Emerging Technologies Machine Learning For Edge AI applications, deploying online learning and adaptation on resource-constrained embedded devices can deal with fast sensor-generated streams of data in changing environments. However, since maintaining low-latency and power-efficient inference is paramount at the Edge, online learning and adaptation on the device should impose minimal additional overhead for inference. With this goal in mind, we explore energy-efficient learning and adaptation on-device for streaming-data Edge AI applications using Spiking Neural Networks (SNNs), which follow the principles of brain-inspired computing, such as high-parallelism, neuron co-located memory and compute, and event-driven processing. We propose EON-1, a brain-inspired processor for near-sensor extreme edge online feature extraction, that integrates a fast online learning and adaptation algorithm. We report results of only 1% energy overhead for learning, by far the lowest overhead when compared to other SoTA solutions, while attaining comparable inference accuracy. Furthermore, we demonstrate that EON-1 is up for the challenge of low-latency processing of HD and UHD streaming video in real-time, with learning enabled. |
| title | EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction |
| topic | Neural and Evolutionary Computing Artificial Intelligence Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2406.17285 |