RISC-Vtimingembedded

Integrating Timing Analysis Into RISC‑V Inference Workflows

UUnknown

2026-02-15

10 min read

Ensure deterministic ML on SiFive RISC‑V by combining RocqStat timing analysis, hardware validation and CI gating for provable WCET and low tail latency.

Hook: Why timing verification is the missing piece for deterministic RISC‑V inference

If you've shipped an embedded ML model on a RISC‑V device only to see unpredictable tail latency, missed deadlines, or sporadic timeouts in the field, you're not alone. Constrained devices amplify timing variability — caches, interrupts, DVFS, and vector units can turn a 10 ms inference into a 50 ms spike that breaks real‑time guarantees. In 2026, edge use cases demand not just throughput but determinism.

This article shows a practical path to deterministic ML inference on SiFive RISC‑V platforms by combining static and formal timing analysis via RocqStat with build and test automation (VectorCAST-style workflows) inside CI/CD. You’ll get concrete toolchain steps, CI examples, measurement recipes and architectural patterns you can apply today.

Why timing analysis matters for embedded ML in 2026

The edge ML landscape in 2026 has three trends that make timing verification essential:

More ML on smaller devices: quantized models and compiler accelerations push complex inference to microcontrollers and tiny SoCs.
RISC‑V adoption and customization: SiFive and other IP providers ship domain‑specific extensions (vector units, domain accelerators) which improve throughput but complicate timing models.
Regulatory and safety expectations: automotive, industrial and medical edge systems increasingly require documented worst‑case execution times (WCET) and deterministic behavior.

In January 2026 Vector Informatik acquired StatInf’s RocqStat timing analysis technology to integrate it into its VectorCAST verification toolchain — a signal that timing verification is moving from niche research tools into mainstream CI and code testing environments. At the same time, SiFive continues to expand its RISC‑V platforms and ecosystem, making rigorous timing verification both feasible and necessary for embedded ML workloads.

Core timing problems for RISC‑V inference

Before diving into the solution, understand the concrete sources of timing non‑determinism you’ll encounter on a SiFive RISC‑V platform:

Microarchitectural variability: caches, TLB misses, branch pipelines and speculative execution introduce data‑dependent latency.
Heterogeneous units: vector (RVV) and custom accelerators have different latencies and sharing policies.
Interrupts and OS jitter: RTOS scheduling, deferred interrupts and background tasks cause preemption.
DVFS and thermal throttling: runtime frequency and voltage scaling change cycle budgets.
Memory contention: DMA, bus masters and shared memory can block inference code unpredictably.

How RocqStat + SiFive creates determinism

Addressing the above requires both modeling and measurement. RocqStat brings advanced WCET and timing analysis: static analysis, path enumeration, and pipeline/caches models. SiFive platforms provide detailed architectural documentation and debug hooks (performance counters, debug modules) that let you correlate models with hardware behavior.

The overall approach is:

Compile a determinism‑friendly inference binary for your SiFive core.
Run static timing analysis (RocqStat) to get WCET estimates for inference kernels and end‑to‑end paths.
Validate with hardware measurements (perf counters, cycle CSRs, vector unit counters).
Feed results into automated tests and gating logic in CI (VectorCAST style) so regressions fail the pipeline.

High‑level toolchain (ASCII diagram)

Model -> Quantize/Compile -> ELF -> RocqStat WCET analysis
                      |                             |
                      v                             v
           VectorCAST unit/integration tests -> CI pipeline -> Hardware regression

Practical integration: step‑by‑step

The next section is a pragmatic recipe you can follow, using common tools (SiFive SDK, riscv GCC/LLVM, RocqStat-style analysis, VectorCAST-style testing) integrated into CI. The examples assume a typical SiFive E/FE series board running bare metal or an RTOS.

1) Prepare deterministic inference code

Prefer integer/fixed‑point operators. Floating point can be deterministic but quantization reduces variability and memory footprint. Use int8/int16 where acceptable.
Eliminate dynamic memory and non‑deterministic allocations. Pre‑allocate buffers and use static memory pools.
Pin inference to a dedicated core or priority. Reserve a core (or lock interrupts) for the real‑time inference path during measurement and in production when determinism is required.
Constrain caches and DMA. If possible, use scratchpad memory or locked cache regions for inference kernels to make access latency predictable.

2) Build with repeatable toolchain flags

Use a deterministic cross toolchain; avoid LTO options that change code layout between builds when you need stable mapping for timing analysis. Example compile commands (adapt to your SDK):

riscv64-unknown-elf-gcc -O2 -g -ffunction-sections -fdata-sections \
  -march=rv32imac -mabi=ilp32 -DNO_DYNAMIC_ALLOC -o build/infer.elf \
  src/infer.c src/ops.c -Wl,--gc-sections -Wl,-Map=build/infer.map

Produce a linker map (infer.map) and ELF; RocqStat and other timing analyzers consume the ELF and map to model the binary layout.

3) Run static timing analysis (RocqStat)

RocqStat performs WCET estimation by combining control‑flow analysis with microarchitectural models. Integrate it as a gating step in CI. Example (pseudo) CLI:

rocqstat analyze \
  --elf build/infer.elf \
  --map build/infer.map \
  --target sifive-e21 \
  --cache-model locked|set-assoc:2:32 \
  --vector-model rvv:128 \
  --output build/wcet.json

Key configuration items for RocqStat:

CPU microarchitecture profile: pipeline stages, functional units, branch prediction, vector unit latency.
Cache model: size, associativity, line size and whether critical regions are locked.
Interrupt model: which IRQs can preempt and their WCET budgets.

4) Validate on hardware

Static analysis needs validation. On SiFive boards use core counters and debug modules to measure cycles and compare to RocqStat WCET. Two approaches:

CSR cycle reads: wrap inference with cycle reads to measure observed latency.

unsigned long long read_cycle() {
  unsigned long long c;
  __asm__ volatile ("csrr %0, cycle" : "=r" (c));
  return c;
}

start = read_cycle();
run_inference();
end = read_cycle();
printf("cycles=%llu", end - start);

Hardware trace + perf counters: use SiFive’s debug interface/OpenOCD or a JTAG probe to collect instruction traces, cache event counters and interrupt logs. Correlate traces to path‑level WCET estimates from RocqStat and feed telemetry into your edge+cloud telemetry pipelines.

If measured times exceed RocqStat estimates, iterate: refine the microarchitecture model, account for shared resource contention, or redesign memory layout to reduce variability.

5) Gate WCET in CI (VectorCAST‑style)

Integrate timing verification as a formalized test stage in your CI pipeline. The pattern below shows a GitHub Actions-style job that runs RocqStat and fails on regression.

name: wcet-check
on: [push, pull_request]
jobs:
  analyze-wcet:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup toolchain
        run: sudo apt-get install riscv64-elf-gcc
      - name: Build
        run: make BUILD_TYPE=ci
      - name: Run RocqStat
        run: |
          rocqstat analyze --elf build/infer.elf --output wcet.json
          cat wcet.json
      - name: Fail on threshold
        run: |
          wcet=$(jq .total_cycles wcet.json)
          if [ "$wcet" -gt 200000 ]; then
            echo "WCET regression: $wcet cycles"; exit 1
          fi

For safety‑critical projects, create VectorCAST-style test suites that pair unit tests with timing assertions, so functional correctness and timing bounds are both enforced before merge. If you need help integrating this into a mature devex and CI system, consider patterns from teams building modern developer platforms (Build a Developer Experience Platform).

Observability: closing the loop between model and hardware

Static analysis is only useful combined with observability. Implement these measurement strategies:

Continuous regression traces: on a nightly job, flash a representative workload and collect cycle counts and trace snippets for key inference paths.
Event tagging: instrument key operators and annotate traces with operator names so you can match WCET path estimates to actual operator timings.
Counter correlation: read cache miss counters, vector unit stalls and memory arbitration counters to explain why an observed run hit the worst‑case path. Feeding these metrics into an observability pipeline helps close the loop between model and hardware.

These metrics become inputs to your timing models. Over time you can build conservative probabilistic models and convert them into hard WCET bounds that RocqStat can reason about.

Case study: deterministic CNN inference on a SiFive E‑class (example)

This is a short, pragmatic example showing how teams have reduced latency variance on constrained devices (a distilled, representative workflow rather than a verbatim customer report).

Target: a quantized 1 MB CNN on a SiFive E-class microcontroller with a small vector unit.
Action: developers locked critical model weights in Tightly Coupled Memory (TCM), disabled preemption for the kernel, and used fixed‑size input buffers to avoid dynamic allocation jitter.
Analysis: RocqStat reported a WCET of 120k cycles for the inference path; initial hardware runs measured 140k cycles due to DMA prefetch stalls.
Fix: refactored DMA timing and added a small cache locking for weight segments. Measured cycles converged to 118–122k and the CI gate ensured no regression.
Outcome: tail latency dropped from 400 ms spikes to 2–3 ms window around the median; the team shipped with WCET documentation for safety certs.

Advanced strategies for 2026 and beyond

As RISC‑V hardware evolves, your timing strategy should advance too. Here are advanced techniques to adopt:

Model hardware accelerators: include accelerator invocation cost and arbitration policies in your timing model so offloaded kernels are bounded.
Use resource reservation: leverage RISC‑V PMP, memory partitioning and RTOS reservations to reduce shared contention windows.
Formalize interrupt budgets: declare and enforce interrupt WCET budgets in the analysis; have the CI enforce the interrupt policy with tests that inject interrupts at worst times.
Adopt hybrid analysis: combine RocqStat static WCET with statistical worst‑case measurements to form robust safety margins (deterministic bound = static WCET + measured contention margin).

Looking ahead, the integration of RocqStat into VectorCAST (following Vector’s 2026 acquisition) will make these workflows native to mainstream code testing and CI. SiFive’s continued expansion (including higher bandwidth interconnects and heterogeneous mesh fabrics) will require even tighter alignment of timing models and hardware documentation.

Checklist: Integrate timing verification into your embedded ML pipeline

Produce deterministic builds: stable linker maps and no dynamic layout changes.
Annotate inference entry/exit points and produce ELFs suitable for analysis.
Run RocqStat (or equivalent WCET tool) to estimate per‑operator and end‑to‑end WCET.
Validate with hardware cycle counters and traces; iterate models until they match.
Automate timing checks as gating tests in CI (fail on regression beyond threshold).
Document WCET and assumptions (cache locks, disabled interrupts, reserved cores) for safety audits and vendor trust reviews (trust scores for telemetry vendors).

Common pitfalls and how to avoid them

Ignoring shared resources: don’t assume single‑core isolation; model DMA and bus masters.
Over‑fitting to microbenchmarks: synthetic microbenchmarks can hide path dependencies. Use real workloads for validation.
Build non‑repeatability: CI must use pinned toolchain versions and deterministic build flags to keep analysis valid over time.
Insufficient margins: WCET is conservative by design — but you still need a safety margin for model or hardware changes.

Final takeaways and next steps

Deterministic ML inference on constrained SiFive RISC‑V platforms is attainable in 2026, but it requires explicit timing verification, hardware validation, and CI enforcement. RocqStat’s timing analysis — now moving toward VectorCAST integration — gives teams the static tools to reason about WCET while SiFive’s platforms provide the hooks needed to validate those models in hardware.

Start small: pick one critical inference kernel, create a locked‑down build, run RocqStat to get a WCET estimate, and validate on a SiFive dev board. Then automate that path in your CI with pass/fail thresholds. Over several iterations you’ll transform unpredictable inference into a certified, auditable timing envelope.

Call to action

Ready to reduce tail latency and prove deterministic behavior for your embedded ML? Start by creating a minimal reproducible inference app for your SiFive board and run static timing analysis. If you need a template, check our example repo (RISC‑V + RocqStat CI patterns) and a prebuilt VectorCAST test harness to get WCET gating into your pipelines. Want help integrating this into your product CI/CD? Contact us for a hands‑on workshop to map your inference pipeline to a verifiable timing model.

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