Simulation of Random Network of Hodgkin and Huxley Neurons with Exponential Synaptic Conductances on an FPGA Platform
Authors: 
Zheming Jin, Hal FinkelAuthors Info & Claims
Zheming Jin, Hal FinkelAuthors Info & ClaimsPages 653 - 657
Abstract
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.
References
[1]
Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Morrison, A., Goodman, P.H., Harris, F.C. and Zirpe, M., 2007. Simulation of networks of spiking neurons: a review of tools and strategies. Journal of computational neuroscience, 23(3), pp.349--398.
[2]
Hodgkin, A.L. and Huxley, A.F., 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology, 117(4), pp.500--544.
[3]
Tyhach, J., Hutton, M., Atsatt, S., Rahman, A., Vest, B., Lewis, D., Langhammer, M., Shumarayev, S., Hoang, T., Chan, A. and Choi, D.M., 2015, September. Arria? 10 device architecture. In Custom Integrated Circuits Conference (CICC), 2015 IEEE (pp. 1--8). IEEE.
[4]
Intel Stratix 10 Product Table. Intel.
[5]
Koch, D., Hannig, F. and Ziener, D. eds., 2016. FPGAs for Software Programmers. Springer.
[6]
Czajkowski, T.S., Aydonat, U., Denisenko, D., Freeman, J., Kinsner, M., Neto, D., Wong, J., Yiannacouras, P. and Singh, D.P., 2012, August. From OpenCL to high-performance hardware on FPGAs. In Field Programmable Logic and Applications (FPL), 2012 22nd International Conference on(pp. 531--534). IEEE.
[7]
Goodman, D.F., Stimberg, M., Yger, P. and Brette, R., 2014. Brian 2: neural simulations on a variety of computational hardware. BMC neuroscience, 15(1), p.P199.
[8]
Stimberg, M., Goodman, D.F. and Nowotny, T., 2018. Brian2GeNN: a system for accelerating a large variety of spiking neural networks with graphics hardware. bioRxiv, p.448050.
[9]
Jin, Z. and Finkel, H., 2019, April. Exploring the Random Network of Hodgkin and Huxley Neurons with Exponential Synaptic Conductances on OpenCL FPGA Platform. In 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (pp. 316--316). IEEE.
[10]
Podobas, A. and Matsuoka, S., 2017, December. Designing and accelerating spiking neural networks using OpenCL for FPGAs. In Field Programmable Technology (ICFPT), 2017 International Conference on (pp. 255--258). IEEE.
[11]
Smaragdos, G., Isaza, S., van Eijk, M.F., Sourdis, I. and Strydis, C., 2014, February. FPGA-based biophysically-meaningful modeling of olivocerebellar neurons. In Proceedings of the 2014 ACM/SIGDA international symposium on Field-programmable gate arrays (pp. 89--98). ACM.
[12]
Cheung, K., Schultz, S.R. and Luk, W., 2016. NeuroFlow: a general purpose spiking neural network simulation platform using customizable processors. Frontiers in neuroscience, 9, p.516.
[13]
La Rosa, M., Caruso, E., Fortuna, L., Frasca, M., Occhipinti, L. and Rivoli, F., 2005, June. Neuronal dynamics on FPGA: Izhikevich's model. In Bioengineered and Bioinspired Systems II (Vol. 5839, pp. 87--95). International Society for Optics and Photonics.
[14]
Thomas, D. and Luk, W., 2009, April. FPGA accelerated simulation of biologically plausible spiking neural networks. In 2009 17th IEEE symposium on field programmable custom computing machines (pp. 45--52). IEEE.
[15]
Ambroise, M., Levi, T., Bornat, Y. and Saighi, S., 2013, March. Biorealistic spiking neural network on FPGA. In 2013 47th Annual Conference on Information Sciences and Systems (CISS) (pp. 1--6). IEEE.
[16]
Jin, Z., Finkel, H., Yoshii, K. and Cappello, F., 2017, August. Evaluation of a Floating-Point Intensive Kernel on FPGA. In European Conference on Parallel Processing (pp. 664--675). Springer, Cham.
[17]
Intel FPGA SDK for OpenCL Programming Guide. UG-OCL002. 2017.05.08
[18]
https://github.com/intel/compute-runtime
[19]
Munshi, A., Gaster, B., Mattson, T.G. and Ginsburg, D., 2011. OpenCL programming guide. Pearson Education.
[20]
Jeffers, J., Reinders, J. and Sodani, A., 2016. Intel Xeon Phi Processor High Performance Programming: Knights Landing Edition. Morgan Kaufmann.
[21]
Jin, Z. and Finkel, H., 2018, May. Performance-oriented Optimizations for OpenCL Streaming Kernels on the FPGA. In Proceedings of the International Workshop on OpenCL (p. 1). ACM.
Index Terms
- Simulation of Random Network of Hodgkin and Huxley Neurons with Exponential Synaptic Conductances on an FPGA Platform
Recommendations
Nuclear Reactor Simulations on OpenCL FPGA Platform
FPGA '19: Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate ArraysField-programmable gate arrays (FPGAs) are becoming a promising choice as a heterogeneous computing component for scientific computing when floating-point optimized architectures are added to the current FPGAs. The maturing high-level synthesis (HLS) ...
On the Portability of the OpenCL Dwarfs on Fixed and Reconfigurable Parallel Platforms
ICPADS '13: Proceedings of the 2013 International Conference on Parallel and Distributed SystemsThe proliferation of heterogeneous computing systems presents the parallel computing community with the challenge of porting legacy and emerging applications to multiple processors with diverse programming abstractions. OpenCL is a vendor-agnostic and ...
Base64 Encoding on OpenCL FPGA Platform
FPGA '19: Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate ArraysBase64 encoding has many applications on the Web. Previous studies are focused on improving the efficiency of Base64 encoding on central processing units (CPUs). As field-programmable gate arrays (FPGAs) are becoming promising heterogeneous computing ...
Comments
Information & Contributors
Information
Published In
September 2019
716 pages
ISBN:9781450366663
DOI:10.1145/3307339
- General Chairs:
- Xinghua (Mindy) Shi,
- Michael Buck,
- Program Chairs:
- Jian Ma,
- Pierangelo Veltri
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 September 2019
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- U.S. Department of Energy Office of Science
Conference
BCB '19
Sponsor:
BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
September 7 - 10, 2019
NY, Niagara Falls, USA
Acceptance Rates
BCB '19 Paper Acceptance Rate 42 of 157 submissions, 27%;
Overall Acceptance Rate 254 of 885 submissions, 29%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 110Total Downloads
- Downloads (Last 12 months)9
- Downloads (Last 6 weeks)2
Reflects downloads up to 07 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in