Improving the Simulation of Biologically Accurate Neural Networks Using Data Flow HLS Transformations on Heterogeneous SoC-FPGA Platforms
This work proposes a hardware performance-oriented design methodology aimed at generating efficient high-level synthesis (HLS) coded data multiprocessing on a heterogeneous platform. The methodology is tested on typical neuroscientific complex application: the biologically accurate modeling of a brain region known as the inferior olivary nucleus (ION). The ION cells are described using a multi-compartmental model based on the extended Hodgkin-Huxley membrane model (eHH), which requires the solution of a set of coupled differential equations. The proposed methodology is tested against alternative HPC implementations (multi-core CPU i7-7820HQ, and a Virtex7 FPGA) of the same ION model for different neural network sizes. Results show that the solution runs 10 to 4 times faster than our previous implementation using the same board and closes the gap between the performance against a Virtex7 implementation without using at full-capacity the AXI-HP channels.
|Dataflow, FPGA, HLS, HPC, Inferior olivary nucleus, Spiking neural networks|
|Communications in Computer and Information Science|
|Organisation||Department of Neuroscience|
Alfaro-Badilla, K. (Kaleb), Arroyo-Romero, A. (Andrés), Salazar-García, C. (Carlos), León-Vega, L.G. (Luis G.), Espinoza-González, J. (Javier), Hernández-Castro, F. (Franklin), … Strydis, C. (2020). Improving the Simulation of Biologically Accurate Neural Networks Using Data Flow HLS Transformations on Heterogeneous SoC-FPGA Platforms. In Communications in Computer and Information Science. doi:10.1007/978-3-030-41005-6_13