Test-Driven Design for AI-Focused Hardware Platforms

Abstract

The paper explores how competitive, technology-agnostic AI-centered hardware can integrate test-driven design (TDD) both within and outside of the system. The method requires writing executable tests before implementation, binding oracles that involve the golden model on the boundary with tensors, operators, and kernels, and continuously integrating and automating regression testing of the simulation, emulation, and prototype targets. Multi-tier stimuli (unit, integration, system) are operationalized, acceptance envelopes created around possible numerical correctness (e.g., 0.5 percentage-point top-1 delta vs. FP32 with INT8/FP16), and the correctness performance and energy gates are linked. Empirically, it has been found that the method can raise pre-tape-out functional coverage to ≥95% (measured at 97.1%), code/toggle coverage to ≥90%, and mean time-to-repair by a factor of ~40% (10.1→6.0 days). In representative workloads (ResNet-50, MobileNet-V3, Transformer, and streaming ASR), the p99 latency is reduced by ~11–13%, throughput is increased by ≥10%, and power usage is decreased by ≥12% (inferences/J) with no notable differences in accuracy. Fault injection and environmental corners are used to increase reliability, and fleet-style telemetry can detect and roll back early issues. Benchmarking is based on MLPerf-style KPIs, and both tails and medians are reported. Additionally, regression bisection is automatically performed to distinguish between compiler and RTL causes. The findings indicate that hardware-centric TDD offers a feasible, supply-side blueprint that reduces the re-spin risk (approximately 15%) and speeds up the time-to-market, with artifacts that are reproducible across data centre and edge deployments. Artifacts and datasets are published to facilitate replication studies.