Abstract
This paper presents algorithm and digital hardware design, inspired by biological spiking neural networks, to perform unsupervised, online spike-clustering with high accuracy and low-power consumption in the context of deep-brain sensing and stimulation systems. The proposed hardware contains 1220 digital neurons and 4.86k latch-based synapses, and achieves the average sorting accuracy of 91% whereas the conventional hardware based on the Osort algorithm achieves 69% for the same datasets. Implemented in a 65nm high-Vth, the processor exhibits a footprint of 0.25mm2/ch. and a power consumption of 9.3μW/ch. at VDD of 0.3V.
Original language | English (US) |
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Title of host publication | Proceedings of the International Symposium on Low Power Electronics and Design |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 91-97 |
Number of pages | 7 |
Volume | 2015-September |
ISBN (Print) | 9781467380096 |
DOIs | |
State | Published - Sep 21 2015 |
Event | 20th IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2015 - Rome, Italy Duration: Jul 22 2015 → Jul 24 2015 |
Other
Other | 20th IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2015 |
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Country | Italy |
City | Rome |
Period | 7/22/15 → 7/24/15 |
Keywords
- Accuracy
- Clustering algorithms
- Encoding
- Firing
- Hardware
- Neurons
- Training
ASJC Scopus subject areas
- Engineering(all)