Bio-inspired system architecture for energy efficient, BIGDATA computing with application to wide area motion imagery

Abstract

In this paper we discuss a brain-inspired system architecture for real-time big velocity BIGDATA processing that originates in large format tiled imaging arrays used in wide area motion imagery ubiquitous surveillance. High performance and high throughput is achieved through approximate computing and fixed point arithmetic in a variable precision (6 bits to 18 bits) architecture. The architecture implements a variety of processing algorithms classes ranging from convolutional networks (Con-vNets) to linear and non-linear morphological processing, probabilistic inference using exact and approximate Bayesian methods and ConvNet based classification. The processing pipeline is implemented entirely using event based neuromorphic and stochastic computational primitives. The system is capable of processing in real-time 160 × 120 raw pixel data running on a reconfigurable computing platform (5 Xilinx Kintex-7 FPGAs). The reconfigurable computing implementation was developed to emulate the computational structures for a 3D System on Chip (3D-SOC) that will be fabricated in the 55nm CMOS technology and it has a dual goal: (i) algorithm exploration and (ii) architecture exploration.