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.

Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experiments. State-of-the-art neuron simulators are, however, capable of simulating at most few tens/hundreds of biophysically accurate neurons in real-time due to the exponential growth in the interneuron communication costs with the number of simulated neurons. In this paper, we propose a real-time, reconfigurable, multichip system architecture based on localized communication, which effectively reduces the communication cost to a linear growth. All parts of the system are generated automatically, based on the neuron connectivity scheme. Experimental results indicate that the proposed system architecture allows the capacity of over 3000 to 19 200 (depending on the connectivity scheme) biophysically accurate neurons over multiple chips.

In-vivo and in-vitro experiments are routinely used in neuroscience to unravel brain functionality. Although they are a powerful experimentation tool, they are also time-consuming and, often, restrictive. Computational neuroscience attempts to solve this by using biologically-plausible and biophysically-meaningful neuron models, most prominent among which are the conductance-based models. Their computational complexity calls for accelerator-based computing to mount large-scale or real-time neuroscientific experiments. In this paper, we analyze and draw conclusions on the class of conductance models by using a representative modeling application of the inferior olive (InfOli), an important part of the olivocerebellar brain circuit. We conduct an extensive profiling session to identify the computational and data-transfer requirements of the application under various realistic use cases. The application is, then, ported onto two acceleration nodes, an Intel Xeon Phi and a Maxeler Vectis Data Flow Engine (DFE). We evaluate the performance scalability and resource requirements of the InfOli application on the two target platforms. The analysis of InfOli, which is a real-life neuroscientific application, can serve as a useful guide for porting a wide range of similar workloads on platforms like the Xeon Phi or the Maxeler DFEs. As accelerators are increasingly populating High-Performance Computing (HPC) infrastructure, the current paper provides useful insight on how to optimally use such nodes to run complex and relevant neuron modeling workloads.

The Inferior-Olivary nucleus (ION) is a well-charted brain region, heavily associated with the sensorimotor control of the body. It comprises neural cells with unique properties which facilitate sensory processing and motor-learning skills. Simulations of such neurons become rapidly intractable when biophysically plausible models and meaningful network sizes (at least in the order of some hundreds of cells) are modeled. To overcome this problem, we accelerate a highly detailed ION network model using a Maxeler Dataflow Computing Machine. The design simulates a 330-cell network at real-time speed and achieves maximum throughputs of 24.7 GFLOPS. The Maxeler machine, integrating a Virtex-6 FPGA, yields speedups of ×92-102, and ×2-8 compared to a reference-C implementation, running on a Intel Xeon 2.66GHz, and a pure Virtex-7 FPGA implementation, respectively.

The Inferior-Olivary nucleus (ION) is a well-charted region of the brain, heavily associated with sensorimotor control of the body. It comprises ION cells with unique properties which facilitate sensory processing and motor-learning skills. Various simulation models of ION-cell networks have been written in an attempt to unravel their mysteries. However, simulations become rapidly intractable when biophysically plausible models and meaningful network sizes (100 cells) are modeled. To overcome this problem, in this work we port a highly detailed ION cell network model, originally coded in Matlab, onto an FPGA chip. It was first converted to ANSI C code and extensively profiled. It was, then, translated to HLS C code for the Xilinx Vivado toolflow and various algorithmic and arithmetic optimizations were applied. The design was implemented in a Virtex 7 (XC7VX485T) device and can simulate a 96-cell network at real-time speed, yielding a speedup of 700 compared to the original Matlab code and 12.5 compared to the reference C implementation running on a Intel Xeon 2.66GHz machine with 20GB RAM. For a 1,056-cell network (non-real-time), an FPGA speedup of 45 against the C code can be achieved, demonstrating the design's usefulness in accelerating neuroscience research. Limited by the available on-chip memory, the FPGA can maximally support a 14,400-cell network (non-real-time) with online parameter configurability for cell state and network size. The maximum throughput of the FPGA IONnetwork accelerator can reach 2.13 GFLOPS.

In this article, new heuristic-search methods and algorithms are presented for enabling highly efficient and adaptive, defect-tolerant multiprocessor arrays. We consider systems where a homogeneous multiprocessor array lies on top of reconfigurable interconnects which allow the pipeline stages of the processors to be connected in all possible configurations. Considering the multiprocessor array partitioned in substitutable units at the granularity of pipeline stages, we employ a variety of heuristic-search methods and algorithms to isolate and replace defective units. The proposed heuristics are designed for off-line execution and aim at minimizing the performance overhead necessarily introduced to the array by the interconnects' latency. An empirical evaluation of the designed algorithms is then carried out, in order to assess the targeted problem and the efficacy of our approach. Our findings indicate this to be a NP-complete computational problem, however, our heuristic-search methods can achieve, for the problem sizes we exhaustively searched, 100% accuracy in finding the optimal solution among 1019 possible candidates within 2.5 seconds. Alternatively, they can provide near-optimal solutions at an accuracy which consistently exceeds 70% (compared to the optimal solution) in only 10-4 seconds.