Verifying AI Chips at RTL: How Foundation Models Are Transforming Design Assurance
Modern AI chips are not general-purpose processors. Their RTL design logic is optimized not for instruction throughput, but for dataflow acceleration, parallel computation, and high-efficiency matrix operations. These chips integrate systolic arrays, sparsity-aware data paths, configurable tensor engines, and hierarchical memory systems. This results in RTL that is dense, pipelined, deeply stateful, and often built on custom compute primitives. Verifying such RTL is a significant engineering challenge – not just in functional correctness, but in timing, numerical behaviour, protocol compliance, and architectural intent. In this context, language-based AI models, specifically foundation models like GPT, are starting to change how verification is approached and executed.
Traditionally, RTL verification involves writing directed and randomized testbenches, assertions, monitors, and coverage models. But when applied to AI chips, these tasks become even more complex. The design space spans multiple data precisions (INT8, FP16, BFLOAT16), parallelism configurations, clock gating schemes, and power/thermal states. Test coverage must account for low-level compute behaviour, data interleaving, scheduler pre-emption, and cross-domain synchronization. It’s not unusual for large verification teams to spend months just constructing the right test infrastructure for a single accelerator block.
Generative AI models trained on hardware design patterns can assist in this process. These models – when fine-tuned on Verilog, System Verilog, UVM, and waveform data – can now produce syntactically correct and functionally relevant verification code. More importantly, they can understand design intent when expressed in human language. For instance, an engineer can describe a desired test scenario such as “verify convolution engine stalls correctly when weights are not prefetched” and the model can generate a constrained random sequence, with monitors to detect the stall event, and assertions to validate downstream impact.
When applied to numerical accuracy verification, these models become even more powerful. AI accelerators often rely on approximate arithmetic: fused multiply-add (FMA), quantized convolution, dynamic scaling, or stochastic rounding. Verifying that these units produce bounded errors under all conditions is non-trivial. AI models can be used to generate assertions that enforce numerical invariants, such as relative error margins, zero-padding behaviour, overflow detection, and underflow boundary checks. They can also help in verifying consistency across bit-width conversions and data recoding logic.
Another use case is hardware-software co-verification, where the LLM interprets both hardware behaviour and software configuration payloads (e.g., from compilers, runtime APIs, or neural network graph descriptions). For example, a compiler might configure a matrix multiplication unit for a 128×128 tile with partial sum accumulation. The model can cross-check that the correct control signals are asserted, that DMA channels are preloaded on time, and that buffer reuse policies are correctly mapped in hardware. In this mode, the AI model acts as a design-aware protocol checker between RTL execution and software control logic.
These AI-powered assistants also play a key role in testbench creation and reuse. With increasing design modularity, testbench infrastructure must be replicated and customized for variations across IP blocks. Foundation models can read IP-XACT metadata, infer interface behaviour, and instantiate tailored UVM components for new test environments. They can analyze past regression reports, identify poorly covered paths, and recommend additional sequences or constraints to increase coverage. This turns testbench creation from a time-consuming manual task into a guided, semi-automated loop.
But perhaps the most immediate and practical benefit comes in debugging failed regressions. Foundation models can assist in parsing waveform data, tracing assertion failures, and summarizing root causes in human-readable terms. Given a failing simulation trace, a model can identify suspect signal transitions, correlate them with control FSM behaviour, and even generate hypotheses for likely causes. In this mode, the model functions as a language-based debug assistant, helping engineers triage issues faster and reducing turnaround time on critical regressions.
These tools also offer cross-layer reasoning capabilities – connecting behaviour across abstraction levels. For AI SoCs, this means mapping high-level graph operations (e.g., fused conv-batch norm-ReLU) to RTL signal transitions, buffering behaviour, and memory coherence protocols. Foundation models are well-suited for this because they can interpret both code and natural language, enabling them to serve as a semantic bridge between architecture documentation, source code, and verification artifacts.
However, these models are not formally sound engines. They do not replace equivalence checking, assertion-based verification, or constrained-random testing infrastructure. Rather, they complement it. AI-generated artifacts – whether testbenches, constraints, or debug summaries – must be reviewed and validated in the standard sign-off flow. Engineers remain responsible for ensuring timing closure, coverage targets, and spec compliance.
At BITSILICA, we are building verification workflows that integrate these language-based tools within structured pipelines. Verification agents powered by foundation models assist in spec interpretation, scenario generation, coverage analysis, and first-level debug – all tightly linked to simulators, waveform tools, and regression systems. We treat these models not as black boxes, but as interactive design copilots – ones that accelerate engineering decision-making while staying grounded in proven verification principles.
In the verification of AI chips, the complexity is no longer just logical – it’s behavioural, numerical, architectural, and systemic. Language-based models offer a new kind of intelligence that operates across all these dimensions. When used well, they don’t just write code – they help us reason through it, validate it, and improve it.
This is what makes them not just assistants – but catalysts – for the next generation of RTL verification.
