A Chip-Level Verification Method for Programmable Vision Chip Based on Deep Learning Algorithms

Abstract

The past five years has witnessed tremendous success of deep learning (DL) algorithm in the computer vision field, attributing to its high degree of accuracy on numerous visual tasks. Unfortunately, for the development of programmable vision chips, algorithm verification remains a major challenge due to the high computational complexity of the DL neural network. In this paper, we propose a novel chip-level verification method to address the common issues including low efficiency and poor reusability in verifying vision chips. In contrast to the block-level verification technique, this method focuses on the rapid implementation of the complete DL algorithm in chip-level verification, fulfilling the advanced demands of vision chip prior to the tape-out. The experiments on MobileNet-v1 indicates the significant reduction of the simulation time and debugging overheads via the proposed verification method.