Synthesis-Free, Flexible and Fast Hardware Library for Biophysically Plausible Neurosimulations

Abstract

Computational neuroscience uses models to study the brain. The Hodgkin-Huxley (HH) model, and its extensions, is one of the most powerful, biophysically meaningful models currently used. The high experimental value of the (extended) Hodgkin-Huxley (eHH) models comes at the cost of steep computational requirements. Consequently, for larger networks, neuroscientists either opt for simpler models, losing neuro-computational features, or use high-performance computing systems. The eHH models can be efficiently implemented as a dataflow application on a FPGA-based architecture. The state-of-the-art FPGA-based implementations have proven to be time-consuming because of the long-duration synthesis requirements. We have developed flexHH, a flexible hardware library, compatible with a widely used neuron-model description format, implementing five FPGA-accelerated and parameterizable variants of eHH models (standard HH with optional extensions: custom ion-gates, gap junctions, and/or multiple cell compartments). Therefore, flexHH is a crucial step towards high-flexibility and high-performance FPGA-based simulations, eschewing the penalty of re-engineering and re-synthesis, dismissing the need for an engineer. In terms of performance, flexHH achieves a speedup of 1,065x against NEURON, the simulator standard in computational neuroscience, and speedups between 8x-20x against sequential C. Furthermore, flexHH is faster per simulation step compared to other HPC technologies, provides 65% or better performance density (in FLOPS/LUT) compared to related works, and only shows a marginal performance drop in real-time simulations.