TY - GEN
T1 - A Programmable CNN Accelerator with RISC-V Core in Real-Time Wearable Application
AU - Pan, Sing Yu
AU - Lee, Shuenn Yuh
AU - Hung, Yi Wen
AU - Lin, Chou Ching
AU - Shieh, Gia Shing
N1 - Funding Information:
This research was supported in part by the Taiwan Semiconductor Research Institute and the Ministry of Science and Technology (MOST), Taiwan, R.O.C., under Grants MOST 111-2221-E-006-194.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper has proposed an epilepsy detection algorithm to identify the seizure attack. The algorithm includes a simplified signal preprocessing process and an 8 layers Convolution Neural Network (CNN). This paper has also proposed an architecture, including a CNN accelerator and a 2-stage reduced instruction set computer-V (RISC-V) CPU, to implement the detection algorithm in real-time. The accelerator is implemented in SystemVerilog and validated on the Xilinx PYNQ-Z2. The implementation consumes 3411 LUTs, 2262 flip-flops, 84 KB block random access memory (BRAM), and only 6 DSPs. The total power consumption is 0.118 W in 10-MHz operation frequency. The detection algorithm provides 99.16% accuracy on fixed-point operations with detection latency of 0.137 ms/class. Moreover, the CNN accelerator has the programable ability, so the accelerator can execute different CNN models to fit various wearable applications for different biomedical acquisition systems.
AB - This paper has proposed an epilepsy detection algorithm to identify the seizure attack. The algorithm includes a simplified signal preprocessing process and an 8 layers Convolution Neural Network (CNN). This paper has also proposed an architecture, including a CNN accelerator and a 2-stage reduced instruction set computer-V (RISC-V) CPU, to implement the detection algorithm in real-time. The accelerator is implemented in SystemVerilog and validated on the Xilinx PYNQ-Z2. The implementation consumes 3411 LUTs, 2262 flip-flops, 84 KB block random access memory (BRAM), and only 6 DSPs. The total power consumption is 0.118 W in 10-MHz operation frequency. The detection algorithm provides 99.16% accuracy on fixed-point operations with detection latency of 0.137 ms/class. Moreover, the CNN accelerator has the programable ability, so the accelerator can execute different CNN models to fit various wearable applications for different biomedical acquisition systems.
UR - http://www.scopus.com/inward/record.url?scp=85146300166&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146300166&partnerID=8YFLogxK
U2 - 10.1109/RASSE54974.2022.9989732
DO - 10.1109/RASSE54974.2022.9989732
M3 - Conference contribution
AN - SCOPUS:85146300166
T3 - RASSE 2022 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Symposium Proceedings
BT - RASSE 2022 - IEEE International Conference on Recent Advances in Systems Science and Engineering, Symposium Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Recent Advances in Systems Science and Engineering, RASSE 2022
Y2 - 7 November 2022 through 10 November 2022
ER -