Functional Test Generation for AI Accelerators using Bayesian Optimization

We propose a black-box optimization method to generate functional test patterns for AI inferencing accelerators. Functional testing is faster than structural testing as scan chains are not used for shifting in patterns and shifting out test responses. Moreover, functional testing reduces "over-testing" by targeting the detection of functionally critical faults for a given application workload. We use Bayesian Optimization for targeted test-image generation for stuck-at faults in a systolic array-based accelerator. Our framework supports test-pattern compaction and leverages various types of error regularization for enforcing functional-likeness of the generated test images. We achieve high fault coverage using a small set of test images for pin-level faults in 16-bit and 32-bit floating-point processing elements of the systolic array achieves high fault coverage with a small set of test images.