How RocqStat Improves Timing Guarantees for ML in Real‑Time Systems

Why WCET still decides whether your embedded ML system is safe — and how RocqStat closes the verification gap

Hook: You can achieve 99th-percentile latency of 20 ms for ML inference in lab runs and still miss an automotive timing budget that guarantees safety on the road. For safety‑critical embedded ML — ADAS, industrial motion control, avionics — the tail (the worst case) matters far more than the median. Missing a worst‑case execution time (WCET) bound can invalidate schedulability proofs, fail certification, and create costly recalls.

In 2026, embedded ML workloads are spreading across heterogeneous silicon: CPUs, NPUs, DSPs and tightly coupled GPUs on RISC‑V SoCs. That heterogeneity increases jitter, introduces new microarchitectural interactions and expands the toolchain surface where timing flaws hide. Vector Informatik’s January 2026 acquisition of StatInf’s RocqStat (to be integrated into VectorCAST) signals a turning point: software verification tools are closing the loop between WCET estimation and traditional code testing (Automotive World, Jan 16, 2026).

Executive summary (most important first)

• WCET is a certification and scheduling requirement for real‑time embedded ML. Safety cases require evidence for the absolute upper bound of execution time, not average latency.
• Embedded ML increases timing variability because of data‑dependent control flow, layer sparsity, cache/memory interference and accelerator scheduling.
• RocqStat provides statistical and hybrid WCET estimation that integrates with software verification workflows to produce conservative, evidence‑based bounds for certification and CI pipelines.
• Practical workflow: instrument inference paths, collect traces on target hardware, model platform interference, generate pWCET/WCET reports, and gate releases with timing‑budget checks in CI.

The problem: why “average latency” fails for embedded ML

Two facts create the timing hazard for embedded ML:

1. ML inference latency is often input‑dependent. Different inputs traverse different activation sparsity, early exits, or conditional post‑processing. That variance breaks assumptions of constant compute cost per inference.
2. Modern embedded hardware is complex: multiple cache levels, out‑of‑order cores, shared memory subsystems, hardware accelerators and DMA. Those elements introduce microarchitectural jitter and cross‑task interference that inflate worst‑case times far beyond observed medians.

Consequences:

• Scheduling proofs (RTA/EDF) require safe upper bounds; using median or p95 as a substitute yields unsound schedules.
• Certification standards (ISO 26262, DO‑178C, IEC 61508) require objective evidence that timing constraints hold under worst‑case conditions.
• Undetected worst‑case outliers cause deadline misses that can cascade into system failures: missed braking events, stale perception frames, or control oscillations.

WCET for ML: specific challenges

Data‑dependent control and sparsity

Pruned and dynamic networks (conditional computation, dynamic quantization) change the instruction trace between inferences. That's good for average speed but makes static analysis conservative and often useless without a hybrid approach.

Accelerator scheduling and driver latency

NPUs and DSPs introduce DMA setup, command queues, and driver overhead. Shared accelerator queues mean contention that is difficult to capture without target‑level measurements or field reviews of the edge appliance stack.

Cache and memory interference

Shared caches, DMA bursts, and bus contention can convert a normally fast path into a slow outlier. On multicore controllers, co‑running tasks (radio stacks, logging) can increase eviction and stall times unpredictably — a class of problems discussed in recent cache and high‑traffic API analyses.

Non‑deterministic runtime systems

Modern RTOS + middleware stacks (ROS2, AUTOSAR Adaptive) add layers where scheduling, preemption, and priority inversion alter perceived latency.

RocqStat’s approach: hybrid, statistical and trace‑driven WCET

RocqStat (StatInf) introduced methods that blend measurement‑based, statistical and formal analysis to produce conservative but usable execution‑time bounds. Vector’s acquisition in January 2026 aims to integrate these methods into VectorCAST and deliver a unified verification toolbox (Automotive World, Jan 16, 2026).

Key techniques RocqStat brings to embedded ML timing analysis:

• Measurement‑based pWCET estimation: Collect a large sample of execution times on the target platform using realistic inputs and worst‑case stimuli, then apply extreme value theory (EVT) and statistical upper‑bounding to compute a probabilistic WCET (pWCET) with a specified exceedance probability.
• Hybrid static+dynamic modeling: Use static control‑flow graph (CFG) analysis to find feasible paths and combine that with measured per‑basic‑block times to assemble pathwise upper bounds. See our notes on indexing manuals for edge-era verification and how manuals/trace mapping accelerate audits.
• Hardware‑aware modeling: Account for cache states, branch predictors and accelerator latency by parametrizing models with microarchitectural state and measuring transitions. This ties into platform power and bus behaviour discussions in edge energy orchestration work.
• Trace correlation and attribution: Link performance counters and execution traces to source code and ML layers so you can attribute delays to specific layers, drivers or OS activity. Observability tooling and exported traces are central to that effort (observability and ETL).

"Timing safety is becoming a critical ..." — Eric Barton, SVP, Code Testing Tools, Vector Informatik (on the RocqStat acquisition, Jan 16, 2026)

Practical checklist: verifying ML inference timing budgets with RocqStat + VectorCAST

The following workflow converts lab‑measured numbers into verifiable timing evidence suitable for schedulability analysis, safety reports and CI gates.

1) Define execution scenarios and safety invariants

• Identify critical inference paths: perception pipeline, decision loop, control loop.
• Define worst‑case input scenarios (adversarial images, sensor noise bursts, peak concurrency).
• Set target exceedance probability (e.g., 10^-6 per mission hour) based on safety goals and standards.

2) Instrument and capture traces on the target

Use a lightweight instrumentation layer and hardware trace when available (ETM/CoreSight, PMU). Capture per‑layer and per‑operator timing, driver interactions, and scheduler events. When possible, deploy the same instrumentation used in compact field reviews or edge appliances (edge appliance field notes) so trace formats and collection policies align with production.

Example C wrapper to time an inference path (microbenchmark mode):

// simple microbenchmark wrapper (POSIX clock_gettime)
  struct timespec t0, t1;
  clock_gettime(CLOCK_MONOTONIC_RAW, &t0);
  run_inference(&input);
  clock_gettime(CLOCK_MONOTONIC_RAW, &t1);
  uint64_t ns = (t1.tv_sec - t0.tv_sec) * 1000000000ULL + (t1.tv_nsec - t0.tv_nsec);
  printf("inference_time_ns=%llu\n", ns);
  

3) Generate representative input corpus

Design inputs that emphasize worst‑case behavior: high complexity scenes, synthetic corner cases, and sequences that exercise early‑exit paths. For perception stacks, use a stratified corpus: common, rare, and adversarial.

4) Run large‑scale measurement campaigns

Execute thousands to millions of runs on the actual target hardware under controlled background load scenarios (idle, nominal concurrency, stressed bus). RocqStat’s statistical pipeline consumes these traces to compute a pWCET bound with a confidence level. For guidance on running repeatable stress tests and infrastructure validation, see related field stress testing and router/stress notes (home routers stress-tested).

5) Hybrid static checks

Use control‑flow analysis to verify that measured traces cover all feasible high‑cost paths. If static analysis finds paths not exercised, create targeted tests to measure them or reason about them conservatively.

6) Produce WCET artifacts for verification

• WCET report with pWCET value and confidence interval
• Trace logs and statistical model parameters (observability exports)
• Mapping to source files and ML layers
• Evidence for CI gating and certification packages (tie into CI/CD governance)

7) Automate in CI/CD

Integrate the measurement job and RocqStat analysis into CI. Fail merges if pWCET regressions exceed thresholds. Sample YAML job pseudocode:

jobs:
    wcet-check:
      runs-on: self-hosted
      steps:
        - name: run target measurement harness
          run: ./run_measurements.sh --runs 10000
        - name: upload traces
          run: ./upload_traces.sh traces/ $(rocqstat analyze --input traces/ --output report.json)
        - name: check thresholds
          run: python tools/check_wcet_report.py report.json --max-ns 25000000
  

To make this repeatable at scale, tie CI gating to your developer productivity signals and cost controls (developer productivity guidance), and maintain a documented verification manual for edge deployments (indexing manuals for the edge era).

Concrete examples (real‑world patterns)

Example A: ADAS neural network on multicore SoC

Problem: intermittent deadline misses in the perception pipeline when radio and logging are active.

RocqStat workflow:

• Measure inference latency across scenarios with co‑running radio and logging tasks.
• Attribute long tails to cache thrashing and DMA bursts from logging.
• Mitigate by partitioning logging traffic and using cache locking on the perception core.
• Re‑measure and produce a tightened pWCET bound for the perception task.

Example B: Industrial control with quantized CNN on an NPU

Problem: large latency spikes correlated with NPU command queue saturation.

Solution path:

• Use driver tracepoints to correlate DMA latency to queue occupancy.
• Throttle non‑critical NPU workloads and schedule critical inferences with queue priority.
• Produce WCET evidence that includes worst‑case accelerator queueing delay.

How to interpret statistical WCET (pWCET) results

Statistical WCET is different from absolute formal bounds. RocqStat’s pWCET gives an upper bound with a quantified probability of exceedance (e.g., the execution time will exceed this bound fewer than 10^-6 times per hour).

When to use pWCET:

• Systems where absolute formal analysis is intractable due to dynamic data dependencies.
• When you can control the input distribution or can artificially stress worst‑case paths.

When to prefer formal WCET:

• Hardest real‑time domains where probabilistic guarantees are unacceptable (certain avionics or nuclear controls).
• Small, static kernels where exhaustive path analysis is tractable.

Toolchain integration: why VectorCAST + RocqStat matters in 2026

Historically, timing analysis tools lived separate from functional verification toolchains. That separation forced manual evidence stitching between test reports and WCET reports. Vector’s acquisition of RocqStat in January 2026 (Automotive World) points to a unified future: run unit/integration tests, trace execution, and compute WCET within the same verification flow. Benefits:

• Single source of truth for test coverage and timing evidence.
• Repeatable, automated gating in CI for both functional regressions and timing regressions.
• Stronger certification packages because traces, tests and timing models are linked.

2026 trends that affect WCET for embedded ML

• Heterogeneous RISC‑V platforms: RISC‑V adoption continues, and vendors (e.g., SiFive) are enabling tighter integration with accelerators. That creates new timing interactions to model.
• Edge NPUs and quantized models: More inference is offloaded to NPUs with driver stacks that must be included in timing budgets (see recent edge appliance field notes).
• Toolchain consolidation: Verification vendors (Vector) are integrating timing analysis into test frameworks, enabling more seamless safety evidence in 2026 (developer productivity).
• Regulatory focus: Regulators increasingly ask for timing evidence for ML components in safety cases; probabilistic methods are now accepted when paired with rigorous measurement methodology.

Advanced strategies to tighten WCET for ML inference

Model‑level changes

• Use deterministic model architectures (avoid data‑dependent branching where possible).
• Prefer static operator layouts over dynamic kernels; prefer fused operators to reduce driver transitions.
• Quantize and prune with an eye on worst‑case operator execution, not just average speed.

System and OS controls

• Pin critical inference threads to isolated cores; disable SMT/hyperthreading if it increases jitter.
• Use cache partitioning or page coloring to reduce cross‑task eviction.
• Assign QoS and bandwidth reservations for DMA and shared buses.

Driver and accelerator tuning

• Expose accelerator queue priority and preemption where supported.
• Use deterministic DMA bursts and guard bands for worst‑case memory access times.

What success looks like: measurable outcomes

• Reduced WCET margin: move from conservative over‑provisioning (e.g., 4x median) to evidence‑based margins (e.g., 1.2x median) without sacrificing safety.
• CI‑gated timing regressions: merges fail if pWCET increases beyond an approved delta (CI/CD governance).
• Certifiable artifacts: a WCET report, coverage evidence and linked traces accepted by certification assessors.

Next steps — concrete actions for engineering teams

1. Start by adding target instrumentation to your inference path and run a measurement campaign (10k–1M runs) under multiple background loads.
2. Use a statistical toolchain (RocqStat or equivalent) to compute pWCET and produce reproducible reports. Preserve raw traces for audits.
3. Integrate timing checks into CI; fail builds on regressions and attach WCET artifacts to merges.
4. Where feasible, reduce variance through model and system design changes (isolation, priority queues, deterministic kernels).
5. For certification, link WCET evidence with functional tests in a unified verification package (VectorCAST + RocqStat will enable that integration).

Final thoughts: worst‑case guarantees are a feature, not an overhead

In real‑time embedded ML, the cost of not proving WCET is unpredictability — and unpredictability is the enemy of safety and cost‑effective deployment. The 2026 market is converging on toolchains that treat timing analysis as first‑class verification. RocqStat’s statistical and hybrid methods, now moving into VectorCAST, let engineering teams replace ad‑hoc latency checks with rigorous, reproducible WCET evidence suitable for CI and certification (Automotive World, Jan 16, 2026).

Adopt a measurement‑first approach, close the loop with hybrid static checks, and automate WCET regression testing in CI. Those steps turn difficult timing problems into manageable engineering tasks — and turn timing guarantees into a competitive advantage.

Call to action

If you’re responsible for embedded ML timing or safety cases, start by running a targeted measurement campaign this week. Build the corpus, capture traces on the target, and generate an initial pWCET using a statistical toolkit. If you want a practical walkthrough tailored to your hardware and RTOS, contact our team for a 1:1 review of your inference path and a recommended WCET verification plan.

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