Layered Verification Strategies for AI Accelerator Hardware: Matrix Engines and SRAM Buffers

Abstract

This article presents a structured approach for verifying artificial intelligence accelerator hardware, with a specific focus on matrix compute engines and SRAM-based local buffers. The verification article is organized into three distinct abstraction layers: functional modeling, micro-architectural validation, and performance-sensitive stress scenarios. Functional modeling establishes mathematical correctness through Python-based golden models and bit-exact comparisons. Micro-architectural validation employs directed and random testing to verify hardware implementation details, pipeline stages, and memory access patterns. Performance-sensitive stress testing evaluates system behavior under realistic workloads using end-to-end neural network inference tests and UVM environments. The article demonstrates how this layered approach successfully identifies subtle synchronization issues, memory coherency violations, and timing errors that conventional methodologies might miss. A detailed case study highlights the detection of a pipeline misalignment bug that occurred only under specific matrix dimension conditions. The framework has been successfully implemented in commercial AI accelerator projects, resulting in improved verification efficiency, enhanced bug detection rates, and reduced development cycles, while enabling verification assets to evolve alongside architectural innovations.