Verification of AI Hardware
As AI systems grow in complexity, ensuring the reliability of AI hardware verification has become a critical challenge. Modern AI engines—powered by neural networks, tensor processors, GPUs, and TPUs—demand verification approaches that guarantee accuracy, latency, and throughput under real-world workloads.
This whitepaper explores the core challenges of verifying AI accelerators, from handling hardware complexities in memory and interconnects, to ensuring functional correctness amid floating-point precision and approximate computing. It also covers the need to validate performance with diverse real-world datasets and accommodate rapidly evolving AI algorithms.
Finally, the paper highlights Imparé’s advanced verification environment, featuring UVM-based testbenches, scoreboards, stimulus generators, interconnect VIPs, and a synthesizable testbench for FPGA acceleration. By combining scalability, modularity, and speed, Imparé enables chipmakers to reduce time-to-market while maintaining rigorous verification quality.
Key Highlights
Analysis of AI hardware verification challenges including functional correctness, hardware complexities, real-world data, and algorithm variability.
Approaches using formal verification, FPGA prototyping, scoreboarding, and UVM testbenches to ensure accuracy and efficiency.
Introduction of Imparé’s verification toolkit with modular VIPs and an industry-first synthesizable testbench for accelerated verification.