A Low-Power Artificial-Intelligence-Based 3-D Rendering Processor With Hybrid Deep Neural Network Computing

Abstract

A low-power artificial intelligence (AI)-based 3-D rendering processor is proposed for metaverse solutions in mobile platforms. It suggests a brain-inspired rendering acceleration architecture designed with a visual perception core. It removes useless computations by realizing 1) spatial attention, 2) temporal familiarity, and 3) top-down attention. The remaining deep neural network (DNN) inference tasks are accelerated by a hybrid neural engine that utilizes both coarse-grained and fine-grained sparsity exploitation simultaneously. It divides the DNN tasks into sparse and dense data and allocates them to the two different neural engines, which focus on zero skipping and data reusability, respectively. Thanks to the centrifugal sampling-based workload prediction, it can dynamically divide DNN computations while minimizing peak signal-to-noise ratio loss caused by the prediction. Fabricated with 28-nm CMOS technology, the processor successfully demonstrates a maximum 118 frames-per-second rendering while consuming 99.95% lower power compared with modern GPUs.