A 125 GOPS 583 mW Network-on-Chip Based Parallel Processor With Bio-Inspired Visual Attention Engine

Abstract

A network-on-chip (NoC) based parallel processor is presented for bio-inspired real-time object recognition with visual attention algorithm. It contains an ARM10-compatible 32-bit main processor, 8 single-instruction multiple-data (SIMD) clusters with 8 processing elements in each cluster, a cellular neural network based visual attention engine (VAE), a matching accelerator, and a DMA-like external interface. The VAE with 2-D shift register array finds salient objects on the entire image rapidly. Then, the parallel processor performs further detailed image processing within only the pre-selected attention regions. The low-latency NoC employs dual channel, adaptive switching and packet-based power man- agement, providing 76.8 GB/s aggregated bandwidth. The 36 mm(2) chip contains 1.9 M gates and 226 kB SRAM in a 0.13 mu m 8-metal CMOS technology. The fabricated chip achieves a peak performance of 125 GOPS and 22 frames/sec object recognition while dissipating 583 mW at 1.2 V.