A task-level pipelined many-SIMD augmented reality processor with congestion-aware network-on-chip scheduler

Abstract

A 36 Heterogeneous multicore processor is proposed to accelerate recognition-based markerless augmented reality. To enable a real-time operation of the proposed augmented reality, task-level pipelined multicore architecture with DLP/TLP optimized SIMD processing elements is implemented. In addition, the multicore employs a congestion-aware network-on-chip scheduler for 2D-mesh network-on-chip to support massive internal data transaction caused by task-level pipeline. As a result, it achieves 1.22TOPS peak performance and 1.57TOPS/W energy-efficiency, which are 88% and 76% improvement over a state-of-the-art augmented reality processor, for 30fps 720p test input video.