Worst-Case Execution Time (WCET) for AI on Edge Devices: Lessons from Vector and RocqStat

Why WCET matters now: the friction point for AI on the edge

Edge AI is moving into safety-critical domains — automotive ADAS, industrial robotics, and avionics — where a missed deadline is a hazard. Developers and DevOps teams face a hard problem: modern neural networks and their runtimes are built for throughput and accuracy, not for deterministic timing. That mismatch creates a core risk for any real-time system that relies on AI inference at the edge.

In late 2025 and early 2026 the industry saw a meaningful shift: Vector Informatik acquired StatInf’s RocqStat to combine timing analysis and worst-case execution time (WCET) estimation with Vector’s VectorCAST toolchain. This move signals that unified verification and timing analysis toolchains are becoming essential for delivering deterministic, certifiable edge AI. If you’re responsible for building or operating safety-critical systems that run AI on edge devices, you must understand WCET, its limitations for AI workloads, and practical mitigation strategies.

What WCET is — and why standard approaches break down for AI

WCET (Worst-Case Execution Time) is the maximum time a particular piece of code can take to execute on a target platform under worst-case conditions. In safety-critical domains, WCET is a fundamental input for schedulability analysis, deadline assignment, and system-level verification with standards such as ISO 26262, DO-178C, and IEC 61508.

Traditional WCET analysis assumes relatively predictable control-flow, bounded loops, and analyzable hardware behavior. Modern AI inference pipelines break these assumptions in multiple ways:

• Data-dependent execution: sparse inputs, conditional operators, and dynamic kernels can cause large variance in runtime.
• Layer and operator diversity: fused operators, hardware-accelerated primitives, and vendor-specific libraries (CUDA, NPU runtimes) produce non-transparent timing.
• JIT and dynamic optimization: runtime kernel selection, JIT compilation, and autotuning introduce variability.
• Hardware microarchitectural effects: caches, speculative execution, and DVFS affect tails and make pure measurement masking fragile.

Vector + RocqStat: what the integration means for developers

Vector will integrate RocqStat into its VectorCAST toolchain to unify timing analysis and software verification.

Vector’s acquisition of RocqStat (announced January 2026) is more than a vendor consolidation: it concretely recognizes timing analysis as a first-class citizen of verification workflows. Combining VectorCAST’s test harness, unit/integration testing and tool qualification capabilities with RocqStat’s timing analytics enables teams to:

• Create traceable, auditable timing reports alongside functional verification artifacts.
• Automate WCET estimation as part of CI and HIL pipelines.
• Correlate code coverage, execution paths and timing hotspots through a single toolchain.

For teams targeting certification, this is significant — it simplifies showing that timing analysis wasn’t an afterthought and that WCET evidence is linked to specific tests and code revisions.

Three approaches to WCET — and when each is appropriate

There are three mainstream approaches to deriving WCET. Each has pros and cons for AI workloads.

1. Static WCET analysis (S-WCET)

Static analyzers reason about all possible control-flow paths and microarchitectural states to produce a guaranteed bound. S-WCET is powerful for control-dominated code (classic embedded systems) but struggles when code uses dynamic dispatch, complex library calls, or deep models implemented in vendor runtimes. For AI, S-WCET only works when the inference pipeline is fully static, with bounded loops and deterministic memory access patterns.

2. Measurement-based WCET (M-WCET)

Measurement approaches execute workloads under stress tests and capture long-run latencies. These are practical for complex code and third-party libraries but cannot provide absolute guarantees — they provide statistical bounds (e.g., 99.999th percentile). For AI this is useful as an empirical check, but you must combine it with conservative margins for certification.

3. Hybrid methods (Static + Measurement)

Hybrid approaches combine static analysis for analyzable parts and measurement for opaque components (e.g., vendor NPUs). RocqStat and integrated toolchains are pushing hybrid adoption, which is currently the most pragmatic path for edge AI — it gives provable bounds where possible and defensible empirical evidence elsewhere.

Concrete challenges when applying WCET to neural inference

Below are the specific technical challenges teams face on edge devices and practical mitigations you can adopt today.

1. Variable inference time due to sparsity and data-dependent execution

Many models exploit sparsity or conditional execution (e.g., dynamic pruning, attention mechanisms). The result: two inputs with identical sizes may trigger very different operator kernels.

• Lock the model graph: export and run static graphs (TFLite flatbuffers, ONNX with fixed operators) and disable dynamic kernels.
• Use fixed-size inputs and pad/normalize to prevent control-flow changes based on dimensions.
• Profile with adversarial inputs that exercise worst-case code paths (not just representative data).

2. Non-deterministic runtimes and vendor optimizations

NPUs, GPUs, and optimized BLAS libraries introduce internal scheduling and proprietary optimizations. They often lack the visibility needed for S-WCET.

• Prefer deterministic inference backends for safety-critical paths (e.g., fixed-point inference engines or certified runtimes).
• Where vendor libraries are required, treat them as black boxes and measure aggressively under worst-case stress and temperature conditions.
• Use vendor profiling and trace hooks (ARM CoreSight, ETM, NPU vendor traces) to obtain fine-grained timing of kernels; couple these traces with network and system observability to diagnose contention.

3. Microarchitectural state: caches, prefetching, and DVFS

Edge SoCs are aggressively power-managed. Frequency scaling and caches create long-tail execution times if not controlled.

• Run WCET tests with DVFS and power-management features disabled in the test harness.
• Use cache locking, isolate CPU cores with affinity, or dedicate an RT core for inference in heterogeneous SoCs.
• Stress-test under cold-start cache/memory states to capture worst-case cache misses.

4. Dynamic memory allocation and garbage collection

Dynamic allocation leads to unbounded latency spikes, especially with fragmented heaps or GC-enabled runtimes.

• Avoid dynamic allocation during inference: pre-allocate buffers and use static memory pools.
• Prefer runtimes that allow explicit memory control (embedded TensorFlow Lite, ONNX Runtime with memory arenas).

Measurement recipe: how to get defensible WCET estimates for AI inference

Use this step-by-step process to obtain a repeatable, auditable WCET estimate for an AI task on an edge target.

1. Define the execution unit: identify the smallest atomic inference task (single-frame inference, preprocessing + inference + postprocess).
2. Instrument and trace: enable high-resolution hardware tracing (CoreSight/ETM, RISC-V PMU) and lightweight tracepoints around the inference boundary.
3. Create worst-case inputs: craft inputs and adversarial sequences that trigger the largest compute, memory, and branching behavior.
4. Control platform variability: disable DVFS, isolate CPUs, fix clock sources and run tests at worst-case temperature or have thermal soak tests.
5. Run stress scenarios: run inference concurrently with background tasks (network, storage) to surface interference and contention effects; combine stress runs with network observability when diagnosing cross-stack impacts.
6. Collect long-run samples: run millions of inferences if feasible and capture tail latencies (99.999th percentile). Use statistical techniques (extreme value theory) to extrapolate tails when necessary.
7. Apply margins and hybrid analysis: combine measurement evidence with static analysis for deterministic parts. Apply conservative safety margins that align with your certification requirements.
8. Link timing evidence to tests: store traces and reports as artifacts in your CI pipeline and correlate them to VectorCAST/RocqStat results when possible; consider integrating these artifacts into your developer experience platform or DevEx workflows for traceability.

CI/CD and verification: integrating timing into delivery pipelines

Timing analysis should not be a manual, late-stage activity. Treat WCET as a first-class CI artifact.

• Automate baseline timing runs on hardware-in-the-loop (HIL) or representative kits (e.g., Raspberry Pi 5 + AI HAT+2 for prototyping). Run these on every merge to detect regressions.
• Gate merges with timing regression thresholds — e.g., if 99.99th percentile latency degrades beyond X%, fail the build.
• Make timing artifacts immutable: store traces, WCET reports and configuration in the same artifact storage used for test results to satisfy traceability demands; couple this with robust artifact storage and CI integration from your DevEx tooling.
• Use scenario-based tests that combine worst-case inputs, system load, and environmental variations (temperature, network jitter).

Case study: prototyping determinism on a Raspberry Pi 5 AI HAT+2

Prototype teams often use cost-effective hardware for early validation. The Raspberry Pi 5 plus the AI HAT+2 (announced late 2025) is a good example: it exposes an inferencing NPU and a real-world stack representative of many SoCs.

When I validated an object-detection inferencing pipeline on this platform, the following checklist produced actionable WCET evidence:

1. Compiled a static TFLite model with post-training quantization and disabled dynamic ops.
2. Pinned inference to a dedicated core and disabled CPU frequency scaling in the boot config.
3. Pre-allocated all tensors; used a fixed memory arena to eliminate heap allocation during inference.
4. Measured with perf counters and obtained kernel-level traces from the NPU driver during stress tests that saturated the memory bus.
5. Ran 10 million inferences across varied temperatures and extracted the 99.999th percentile to define operational deadlines.

Outcome: the hybrid measurement/static approach revealed a rare kernel scheduling conflict that increased tail latency by ~3x, a problem we resolved by changing NPU isolation and scheduling in the driver. Without the long-run traces and stress conditions, the issue never appeared in baseline tests.

How to interpret WCET in system design and scheduling

WCET is an input, not a spec. Use it to drive system-level decisions:

• Set deadlines: schedule tasks so that the sum of WCETs on a core leaves room for interrupts and scheduling overhead.
• Dimension slack: in safety domains, design a margin (e.g., 20–50%) above observed WCET based on your S-RAS (safety requirements) and standards guidance.
• Decompose tasks: break AI pipelines into deterministic preprocessing and isolated inference tasks. If the inference is the only non-deterministic part, bounding becomes easier.

Verification, traceability and certification

For regulated systems, you must show that WCET evidence is repeatable and linked to code and tests. That is where integrated toolchains matter. A unified flow linking unit tests, integration tests, coverage, and WCET evidence simplifies auditors' work and reduces manual artifacts.

VectorCAST’s existing strengths in test qualification, combined with RocqStat’s timing analysis, will allow teams to produce consolidated evidence: verified test cases + timing reports + trace artifacts. For teams targeting ISO 26262 ASIL D or DO-178C Level A, this consolidated evidence reduces the certification friction.

Future trends and practical predictions for 2026 and beyond

Based on late-2025 / early-2026 developments, expect these trends:

• Consolidation of timing and verification toolchains: acquisitions like Vector/RocqStat accelerate toolchain integration; expect tighter CI hooks and standardized timing artifacts.
• Deterministic AI runtimes: vendors will release safety-focused inference runtimes with deterministic kernels, fixed memory models, and tool-supported analyzability.
• Hybrid formal methods for AI: combining model-level formal verification (e.g., bounded-time guarantees) with system-level WCET will grow as a practice.
• Edge hardware that exposes richer telemetry: SoCs will provide more deterministic NPU scheduling APIs and better traceability to support certification; tie those advances to your edge-cloud telemetry strategy.

Actionable checklist: apply WCET best practices to your next edge-AI project

• Lock graph and operators: prefer static graphs and fixed operators; avoid runtime JITs on the safety path.
• Eliminate dynamic allocation during inference and use pre-allocated arenas.
• Control the platform: disable DVFS, isolate cores, and perform tests under worst-case thermal and power states.
• Instrument deeply: use hardware tracing, PMUs and vendor traces to collect long-tail latency data; integrate this telemetry with your observability stack (network observability and edge telemetry).
• Adopt hybrid analysis: static where possible, measurement where necessary. Use tools that link artifacts (VectorCAST + RocqStat-style workflows).
• Automate timing tests in CI: run baseline and regression timing tests on HIL and gate on regressions; make these part of your DevEx and CI/CD pipelines.
• Document margins: record how you derive safety margins and ensure traceability to tests and code commits for audits.

Closing: determinism is a design constraint, not an afterthought

WCET for AI on edge devices is difficult but tractable. The challenges are technical and organizational: the technical work is about forcing determinism into inference paths and controlling platform variability; the organizational work is about integrating timing analysis into verification and CI so timing becomes a continuous artifact, not a late-stage checkbox.

Vector’s integration of RocqStat into VectorCAST underscores an industry shift: timing and verification will be delivered together, and teams that adopt unified toolchains and hybrid analysis will be the ones that can safely deploy AI at the edge in 2026 and beyond.

Next steps: start by running a hybrid WCET assessment for your critical AI path: instrument, stress and measure; then apply static analysis to the deterministic portions and consolidate artifacts in your CI. If you need a practical template to run your first measurement campaign or to integrate timing checks into VectorCAST, reach out to the engineering community or add timing artifacts to your next sprint’s Definition of Done.

Call to action

Ready to harden your edge AI pipeline? Download our WCET checklist and CI integration template for timing analysis (VectorCAST + RocqStat friendly). Or share your deployment constraints and we’ll suggest a tailored measurement recipe you can run on your hardware. Start treating timing as code — not as guesswork.

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