embeddedoptimizationverification

Integrating Timing Analysis into Model Compression Workflows for Embedded Devices

UUnknown

2026-02-19

9 min read

Combine quantization and pruning with WCET (RocqStat) analysis—ensure compressed models meet real-time embedded constraints.

Hook: When smaller models break real-time guarantees

You're under a hard deadline: shrink a model for an embedded controller, get power and memory down, and still meet a sub-10ms control loop. You run pruning and int8 quantization, the average latency drops—but a rare execution path suddenly misses a deadline in field tests. That missed deadline is a system failure in safety-critical domains. This article gives you a practical, engineer-first playbook to combine model compression (quantization and pruning) with worst-case execution time (WCET) analysis—using tools such as RocqStat—so compressed models reliably meet real-time constraints on embedded targets.

Why timing-aware compression matters in 2026

Through 2025–2026 we’ve seen two parallel trends: aggressive on-device compression to fit advanced models into constrained hardware, and rising regulatory and engineering emphasis on demonstrable timing safety for embedded AI. In January 2026, Vector Informatik announced the acquisition of StatInf’s RocqStat technology—highlighting the demand for integrated timing verification alongside software testing. Vector plans to fold RocqStat into its VectorCAST toolchain, signaling that timing analysis needs to be part of modern model delivery pipelines, not an afterthought.

"Timing safety is becoming a critical ..." — Eric Barton, Vector (statement on RocqStat integration), January 2026

Key takeaway: Average latency improvements are not sufficient. You must measure and verify worst-case behavior after every compression change.

How compression changes WCET: the technical reality

Compression techniques influence WCET in non-obvious ways. Understand these mechanisms so you can design pipelines that reduce both average and worst-case time:

Quantization: lowers arithmetic precision (e.g., FP32 → INT8). This often reduces compute time and memory footprint, but can introduce different code paths (e.g., quantized kernels, dequantize/quantize bridges) and change cache behavior.
Unstructured pruning: removes individual weights to create sparse tensors. While parameter count drops, irregular memory accesses and sparse kernel overheads can increase WCET due to unpredictable memory indirections.
Structured pruning (channel/block pruning): removes entire filters or blocks. It creates smaller dense operators that are compiler-friendly and generally improves WCET predictability.
Operator fusion and compilation: compilers like TVM, XLA, or vendor toolchains can fuse ops and generate highly optimized code. Fusion affects WCET by removing intermediate memory writes but also changes control flow and timing paths that static analysis tools must consider.
Runtime variability: dynamic operators, conditional execution (e.g., early-exit models), or non-deterministic libraries introduce alternate execution paths and complicate WCET estimation.

Practical workflow: integrate compression with WCET analysis

Below is a step-by-step workflow you can adopt and integrate into CI/CD to ensure compressed models meet deadlines. The flow assumes you target an embedded CPU or accelerator and will produce an executable artifact analyzable by a WCET tool like RocqStat.

Step 0 — Define constraints early

Establish a timing budget per inference (e.g., 5 ms hard deadline) and an acceptable margin for WCET (e.g., 10–20% headroom).
Document target hardware, OS (bare-metal, RTOS, Linux), interrupt model, and worst-case background load assumptions.

Step 1 — Baseline: profile and get a reference WCET

Create a baseline using the uncompressed model. Produce two artifacts:

Measured worst-case from stress testing (measurement-based WCET).
Static WCET using RocqStat (or equivalent) on the compiled code.

These baselines are your reference for regression checks after every compression step.

Step 2 — Choose compression methods with timing in mind

Prefer techniques that improve predictability:

Structured pruning (channel or block) to keep dense compute.
Quantization-aware training (QAT) for highest accuracy at int8 while avoiding mixed-kernel fallbacks.
Block-sparse or low-rank decompositions for predictable memory access.

Step 3 — Create candidate artifacts and compile for the target

After compression, convert model formats and compile to target-optimized code (TFLite for microcontrollers, TensorRT for GPUs, TVM or vendor SDK for embedded accelerators). Ensure the build produces C/C++ objects or linked binaries that a WCET analyzer can consume.

Example pipeline (PyTorch → ONNX → TVM → C):

# Export PyTorch to ONNX
import torch
model = torch.load('model.pt').eval()
dummy = torch.randn(1, 3, 224, 224)
torch.onnx.export(model, dummy, 'model.onnx', opset_version=14)

# Use TVM to compile for your CPU/accelerator
# (TVM scripts omitted for brevity — generate C runtime artifacts)

Step 4 — Run static WCET (RocqStat) on compiled binaries

Static WCET tools require analyzable machine code and control-flow information. The general steps:

Produce a linkable binary with symbol information and no dynamic runtime dependencies.
Provide hardware timing models (instruction timings, cache/memory latencies) to RocqStat.
Annotate or isolate ISR and background tasks so the WCET model reflects actual environment assumptions.

RocqStat specializes in WCET estimation for safety-critical software. With Vector's 2026 acquisition, expect tighter integrations into software testing toolchains (e.g., VectorCAST) making automation easier.

Step 5 — Complement static results with measurement-based stress tests

Static WCET gives a conservative upper bound. Validate it with worst-case measurement runs on hardware (or cycle-accurate simulators):

Use adversarial inputs (max-sparsity, worst memory access patterns) and adversarial scheduling to induce worst-case behavior.
Record timestamps and hardware performance counters (cache misses, page faults, DMA stalls).
Repeat with background load combinations that match system assumptions.

Step 6 — Iterate: tune compression with WCET feedback

When WCET exceeds budget, use targeted adjustments:

Switch from unstructured to structured pruning.
Adjust quantization strategy (use symmetric quantization or prefer int8 kernels with vendor support).
Force operator fusion or change layout to improve cache locality.
Limit dynamic allocation paths and remove variable-length loops in the inference runtime.

Step 7 — Gate in CI/CD

Automate: every model PR triggers compression, compile, static WCET analysis, and fails the build if WCET > budget. Store reports as artifacts for audits.

# Example GitLab CI job snippet
stages:
  - compress
  - compile
  - wcet

compress_model:
  stage: compress
  script:
    - python compress.py --method structured_prune --target 0.5

compile_for_target:
  stage: compile
  script:
    - bash build_target.sh  # outputs binary: inference.bin

wcet_analysis:
  stage: wcet
  script:
    - rocqstat analyze --binary=build/inference.bin --platform=target.yaml --output=wcet_report.json
    - python check_wcet.py wcet_report.json --threshold_ms 5
  artifacts:
    paths: [wcet_report.json]
  allow_failure: false

Concrete examples and snippets

Below are short examples showing how to do the common compress+compile steps and what to watch for.

1) Structured pruning with PyTorch

import torch
import torch.nn.utils.prune as prune

model = MyNet()
# prune entire channels in Conv2d layers
for name, module in model.named_modules():
    if isinstance(module, torch.nn.Conv2d):
        prune.ln_structured(module, name='weight', amount=0.3, n=2, dim=0)  # channel prune

# Remove re-parametrization and save a dense, smaller model
for module in model.modules():
    if isinstance(module, torch.nn.Conv2d):
        prune.remove(module, 'weight')

torch.save(model.state_dict(), 'pruned_model.pt')

Why structured pruning: maps to smaller dense kernels that compilers and WCET analyzers can reason about.

2) Quantization-aware training and export

# PyTorch QAT (simplified)
import torch.quantization as tq
model.train()
model.qconfig = tq.get_default_qat_qconfig('fbgemm')
model_fused = torch.quantization.fuse_modules(model, [['conv', 'relu']])
model_prepared = tq.prepare_qat(model_fused)
# Train for a few epochs, then convert
model_prepared.eval()
model_int8 = tq.convert(model_prepared)
torch.save(model_int8.state_dict(), 'model_int8.pt')

3) Export to ONNX then compile to static C for WCET

Export, then use a toolchain (TVM, Glow, vendor SDK) to compile with a static runtime. Ensure no dynamic linking and include symbol tables for analysis.

WCET-specific best practices for model compression

Prefer compiler-friendly formats: Dense layouts, fixed-size tensors, and fused ops reduce alternative control flows.
Avoid or bound dynamic control: No variable loop counts or data-dependent branching inside the inference path.
Keep memory behavior predictable: pre-allocate buffers, align tensors, and use contiguous layouts to reduce cache unpredictability.
Use structured sparsity: block or channel sparsity yields better timing predictability than element-wise sparsity.
Align compression decisions with accelerator kernels: confirm vendor libraries have optimized kernels for your quantization/pruning choices—fallback kernels are often slower and increase WCET.
Document and version hardware timing models: WCET depends on CPU microarchitectural parameters—store and track the versions of timing models you used for verification.
Keep WCET reports as first-class artifacts: include them in release bundles and safety cases (ISO 26262, DO-178C where applicable).

Dealing with the surprising cases: when compression increases WCET

It happens: unstructured pruning made your model smaller but you see a higher WCET. What to do:

Examine performance counters: look for cache miss spikes, stalls, or increased branch mispredictions.
Run RocqStat with different microarchitectural models to understand which resource dominates the bound.
Consider converting unstructured sparsity to block-sparse patterns or replace with low-rank approximation.
If quantization introduced dequantize/requantize bridges, force the toolchain to use native int8 kernels or fuse bridges away.

2026 trends and future predictions

Here are patterns to watch:

Tighter toolchain integration: Vector’s acquisition of RocqStat accelerates the bundling of timing analysis into mainstream verification suites (VectorCAST). Expect WCET checks to become standard CI gates for embedded ML in regulated industries.
Hardware-aware compression: AutoML and compression tools will increasingly optimize for WCET, not just accuracy and model size.
Hybrid analysis: Static WCET + worst-case measurement pipelines will be integrated, with machine-readable verification artifacts used in safety cases.
Regulatory pressure: Automotive and avionics sectors will tighten requirements for demonstrable timing safety of perception and decision models.

Checklist: Ready-to-deploy pipeline for WCET-aware compression

Define timing budgets and margin.
Baseline WCET (static + measurement) for uncompressed model.
Prefer structured pruning and QAT for quantization.
Compile to static runtime artifacts analyzable by RocqStat.
Run RocqStat and measurement stress tests for each candidate.
Automate in CI/CD with gates and store reports as artifacts.
Keep hardware timing models and toolchain versions under version control.

Closing: actionable takeaways

Don’t assume: A smaller model reduces WCET. Test and verify by design.
Design for determinism: Structured sparsity, fused ops, and fixed tensor sizes lead to predictable worst-case timing.
Automate verification: Make WCET analysis part of your CI/CD; gate deployments on WCET thresholds.
Use the right tools: Static WCET analysis (RocqStat) + measurement-based testing form a defensible verification strategy in 2026’s regulatory climate.

Call to action

If you’re piloting embedded AI or scaling compressed-model delivery across teams, integrate WCET checks into your compression pipelines now. Start by adding a RocqStat run to your CI, prefer structured compression, and baseline your hardware timing models. Need a reproducible environment to test this workflow end-to-end? Try a hosted lab with preconfigured cross-compilers, TVM toolchains, and RocqStat-ready builds—reach out to discuss a pilot and we’ll help you automate WCET-aware model compression and verification for your target platform.

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