Architectural Design and Complexity Analysis of Large-Scale Cortical Simulation on a Hybrid Computing Platform

Abstract

Research and development in modeling and simulation of human cognizance functions requires a high-performance computing platform for manipulating large-scale mathematical models. Traditional computing architectures cannot fulfill the attendant needs in terms of arithmetic computation and communication bandwidth. In this work, we propose a novel hybrid computing architecture for the simulation and evaluation of large-scale associative neural memory models. The proposed architecture achieves very high computing and communication performances by combining the technologies of hardware-accelerated computing, parallel distributed data operation and the publish/subscribe protocol. Analysis has been done on the computation and data bandwidth demands for implementing a large-scale brain-state-in-a-box (BSB) model. Compared to the traditional computing architecture, the proposed architecture can achieve at least 100X speedup.