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.

A heterogeneous hardware-software system implemented on an Avnet ZedBoard Zynq SoC platform, is proposed for the computation of an extended Hodgkin Huxley (eHH), biologically plausible neural model. SoC's ARM A9 is in charge of handling execution of a single neuron as defined in the eHH model, each with a O(N) computational complexity, while the computation of the gap-junctions interactions for each cell is offloaded on the SoC's FPGA, cutting its O(N²) complexity by exploiting parallel-computing hardware techniques. The proposed hw-sw solution allows for speed-ups of about 18 times visa-vis à vectorized software implementation on the SoC's cores, and is comparable to the speed of the same model optimized for a 64-bit Intel Quad Core i7, at 3.9GHz.