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An Inference Hardware Accelerator for EEG-based Emotion Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gonzalez, Hector A. | - |
dc.contributor.author | Muzaffar, Shahzad | - |
dc.contributor.author | Yoo, Jerald | - |
dc.contributor.author | Elfadel, Ibrahim (Abe) M. | - |
dc.date.accessioned | 2024-05-03T04:32:42Z | - |
dc.date.available | 2024-05-03T04:32:42Z | - |
dc.date.created | 2024-05-01 | - |
dc.date.created | 2024-05-01 | - |
dc.date.created | 2024-05-01 | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE International Symposium on Circuits and Systems proceedings, p. 9180728 | - |
dc.identifier.issn | 0271-4302 | - |
dc.identifier.uri | https://hdl.handle.net/10371/200811 | - |
dc.description.abstract | The wearability of emotion classifiers is a must if they are to significantly improve the social integration of patients suffering from neurological disorders. Such wearability requires the use of low-power hardware accelerators that would enable near real-time classification and extended periods of operations. In this paper, we architect, design, implement, and test a handcrafted, hardware Convolutional Neural Network, named BioCNN, optimized for EEG-based emotion detection and other similar bio-medical applications. The architecture of BioCNN is based on aggressive pipelining and hardware parallelism that maximizes resource re-use and minimizes memory footprint. The FEXD and DEAP datasets are used to test the BioCNN prototype that is implemented using the Digilent Atlys Board with a low-cost Spartan-6 FPGA. The experimental results show that BioCNN has a competitive energy efficiency of 11GOps/W, a throughput of 1.65GOps that is in line with the real-time specification of a wearable device, and a latency of less than 1ms, which is much smaller than the 150ms required for human interaction times. Its emotion inference accuracy is competitive with the top software-based emotion detectors. | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | An Inference Hardware Accelerator for EEG-based Emotion Detection | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ISCAS45731.2020.9180728 | - |
dc.citation.journaltitle | IEEE International Symposium on Circuits and Systems proceedings | - |
dc.identifier.wosid | 000696570700341 | - |
dc.identifier.scopusid | 2-s2.0-85109340412 | - |
dc.citation.startpage | 9180728 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Yoo, Jerald | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
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- College of Engineering
- Department of Electrical and Computer Engineering
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