TY - GEN
T1 - Simulation of random network of Hodgkin and Huxley neurons with exponential synaptic conductances on an FPGA platform
AU - Jin, Zheming
AU - Finkel, Hal
N1 - Publisher Copyright:
© 2019 Association of Computing Machinery.
PY - 2019/9/4
Y1 - 2019/9/4
N2 - Field-programmable gate arrays (FPGAs) are becoming a promising choice as a heterogeneous computing component when floating-point optimized architectures are added to the current FPGAs. The maturing high-level synthesis tools offer a streamlined design flow for researchers to develop a parallel application using a high-level language on FPGAs. In this paper, we choose a random network of Hodgkin–Huxley (HH) neurons with exponential synaptic conductance to evaluate the performance of the simulation of networks of spiking neurons on an FPGA. Focused on the conductance-based HH benchmark, we execute the benchmark on a general-purpose simulator for spiking neural networks, identify a computationally intensive kernel in the generated C++ code, convert the kernel to a portable OpenCL kernel, and describe the optimizations which can reduce the resource utilizations and improve the kernel performance. We evaluate the kernel on an Intel Arria 10 based FPGA platform, an Intel Xeon 16-core CPU, an Intel Xeon 4-core low-power processor with a CPU and a GPU integrated on the same chip, and an NVIDIA Tesla P100 discrete GPU. For the kernel execution time, the Arria 10 GX1150 FPGA is 2X and 3X faster than the two CPUs, but it is 2.5X and 4.8X slower than the two GPUs, respectively. The FPGA consumes the least power, but its performance per watt is 1.56X and 1.96X lower than the two GPUs, respectively.
AB - Field-programmable gate arrays (FPGAs) are becoming a promising choice as a heterogeneous computing component when floating-point optimized architectures are added to the current FPGAs. The maturing high-level synthesis tools offer a streamlined design flow for researchers to develop a parallel application using a high-level language on FPGAs. In this paper, we choose a random network of Hodgkin–Huxley (HH) neurons with exponential synaptic conductance to evaluate the performance of the simulation of networks of spiking neurons on an FPGA. Focused on the conductance-based HH benchmark, we execute the benchmark on a general-purpose simulator for spiking neural networks, identify a computationally intensive kernel in the generated C++ code, convert the kernel to a portable OpenCL kernel, and describe the optimizations which can reduce the resource utilizations and improve the kernel performance. We evaluate the kernel on an Intel Arria 10 based FPGA platform, an Intel Xeon 16-core CPU, an Intel Xeon 4-core low-power processor with a CPU and a GPU integrated on the same chip, and an NVIDIA Tesla P100 discrete GPU. For the kernel execution time, the Arria 10 GX1150 FPGA is 2X and 3X faster than the two CPUs, but it is 2.5X and 4.8X slower than the two GPUs, respectively. The FPGA consumes the least power, but its performance per watt is 1.56X and 1.96X lower than the two GPUs, respectively.
KW - CPU
KW - FPGA
KW - GPU
KW - OpenCL
KW - Simulation
KW - Spiking neural network
UR - http://www.scopus.com/inward/record.url?scp=85073172228&partnerID=8YFLogxK
U2 - 10.1145/3307339.3343460
DO - 10.1145/3307339.3343460
M3 - Conference contribution
AN - SCOPUS:85073172228
T3 - ACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
SP - 653
EP - 657
BT - ACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
PB - Association for Computing Machinery, Inc
T2 - 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2019
Y2 - 7 September 2019 through 10 September 2019
ER -