Optimal mapping of inferior olive neuron simulations on the Single-Chip Cloud Computer

Biologically accurate neuron simulations are increasingly important in research related to brain activity. They are computationally intensive and feature data and task parallelism. In this paper, we present a case study for the mapping of a biologically accurate inferior-olive (InfOli), neural cell simulator on an many-core research platform. The Single-Chip Cloud Computer (SCC) is an experimental processor created by Intel Labs. The target neurons provide a major input to the cerebellum and are involved in motor skills and space perception. We exploit task-and data-partitioning, scaling the simulation over more than 40,000 neurons. The voltage-and frequency-scaling capabilities of the chip are explored, achieving more than 20% energy savings with negligible performance degradation. Four platform configurations are evaluated and a mapping with balanced workload and constant voltage and frequency is formally derived as optimal

A real-time hybrid neuron network for highly parallel cognitive systems

For comprehensive understanding of how neurons communicate with each other, new tools need to be developed that can accurately mimic the behaviour of such neurons and neuron networks under 'real-time' constraints. In this paper, we propose an easily customisable, highly pipelined, neuron network design, which executes optimally scheduled floating-point operations for maximal amount of biophysically plausible neurons per FPGA family type. To reduce the required amount of resources without adverse effect on the calculation latency, a single exponent instance is used for multiple neuron calculation operations. Experimental results indicate that the proposed network design allows the simulation of up to 1188 neurons on Virtex7 (XC7VX550T) device in brain real-time yielding a speed-up of x12.4 compared to the state-of-the art