Testing of Neuromorphic Circuits: Structural vs Functional

摘要

Deep neural networks have shown great potential in solving difficult cognitive problems such as object recognition and classification. As a result, several neuromorphic circuits are designed and fabricated to perform various cognitive tasks. While these circuits are subjected to manufacturing defects and runtime failures, a proper testing method for neuromorphic circuits should exploit their inherent tolerance to inaccuracies in order to reduce the cost and complexity of post-manufacturing testing. This paper investigates structural and functional testing methodologies for neuromorphic circuits. The proposed test methodologies allow to significantly reduce the number of faults to be tested, and test time accordingly, without sacrificing the output accuracy or fault coverage.