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An Inference Hardware Accelerator for EEG-based Emotion Detection

Cited 5 time in Web of Science Cited 5 time in Scopus

Gonzalez, Hector A.; Muzaffar, Shahzad; Yoo, Jerald; Elfadel, Ibrahim (Abe) M.

Issue Date
IEEE International Symposium on Circuits and Systems proceedings, p. 9180728
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.
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Yoo, Jerald유담
  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area Biomedical Applications, Energy-Efficient Integrated Circuits


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