embeddedrisc-vverification

Embedded AI Verification: Running RocqStat on RISC-V Platforms

UUnknown

2026-02-21

11 min read

Practical guide to run RocqStat WCET timing analysis on SiFive RISC‑V platforms, with integration tips, RVV and NVLink gotchas, and CI best practices.

Hook: Why timing verification on RISC-V matters for embedded AI

If you’re building embedded AI on SiFive RISC‑V silicon, you already wrestle with heterogeneous compute, tight latency budgets and opaque microarchitectural behavior. The last thing you need is uncertainty in worst‑case execution time (WCET). RocqStat—now part of Vector’s tooling story—offers advanced static timing analysis, but running it accurately on RISC‑V SoCs requires careful setup, target models and integration work. This guide gives a practical, step‑by‑step path to run RocqStat on SiFive platforms, integrate timing analysis into your verification pipeline and avoid common gotchas in 2026’s embedded AI landscape.

Executive summary: What you’ll get

This article gives an actionable workflow to: (1) prepare RISC‑V artifacts for RocqStat, (2) build or adapt a microarchitecture/timing model for SiFive cores, (3) combine static estimates with hardware measurements, (4) integrate results with VectorCAST and CI, and (5) handle advanced cases like RVV (RISC‑V Vector) and GPU/NVLink offload. It reflects developments through late 2025 and early 2026—including Vector’s acquisition of RocqStat technology and SiFive’s NVLink plans—and focuses on reproducible techniques you can apply today.

The 2026 context: why this step matters now

Two industry moves make timing verification on RISC‑V critical in 2026. First, Vector acquired StatInf’s RocqStat technology in January 2026 to integrate timing analysis directly into VectorCAST, signaling that timing safety is moving into mainstream verification workflows (Automotive World, Jan 2026). Second, SiFive’s announced integration of NVIDIA NVLink Fusion into RISC‑V IP means RISC‑V SoCs are increasingly heterogeneous—CPU cores, vector units, and GPU interconnects—making cross‑domain timing analysis essential (Forbes/Tech press, Jan 2026).

Who this guide is for

Embedded software engineers targeting SiFive RISC‑V cores (U‑class, E‑class, or S‑class).
Verification engineers integrating WCET and timing safety into VectorCAST or CI pipelines.
System architects measuring end‑to‑end latency in embedded AI workloads that use RVV or GPU offload.

Preliminaries: tools and materials you’ll need

RocqStat license / CLI tools (or the VectorCAST builds that include RocqStat after integration).
SiFive hardware (e.g., SiFive HiFive or custom SiFive SoC) or a trusted simulator (SiFive Freedom™ emulator, Spike + additions, or cycle‑accurate vendor models).
RISC‑V GNU toolchain or LLVM cross toolchain used to build the target binary.
OpenOCD or debug probe supporting RISC‑V (for hardware trace and counter readouts).
Access to microarchitectural documentation for your SiFive core: pipeline stages, cache sizes/latency, prefetch behavior and vector unit microops.

High‑level workflow (inverted pyramid)

Build a stable, linkable RISC‑V binary with symbols and deterministic layout.
Create or adapt a RocqStat target model describing pipeline and memory hierarchy.
Run static WCET analysis and collect results (basic safety bound).
Validate and refine models with hardware measurements using cycle counters or simulator traces.
Integrate RocqStat results into VectorCAST/CI for gated verification and regression monitoring.

Step 1 — Build binaries that are analyzable

Static timing analysis depends on a deterministic binary format and symbolic metadata. Your compiler/linker flags should prioritize stable layout and preserve function boundaries so RocqStat can map control‑flow and basic blocks accurately.

Recommended build flags

-g (retain symbols)
-O2 or -Os for release vectors, but consider -Og when iterating timing models
-fno-inline-functions (or limit aggressive inlining) for clearer CFGs
-ffunction-sections -fdata-sections and -Wl,--gc-sections carefully: avoid stripping functions you want to analyze
Produce a linker map file: -Wl,-Map=output.map

Example: cross‑compile with riscv64 toolchain:

riscv64-unknown-elf-gcc -march=rv64imafdc -mabi=lp64d -O2 -g -ffunction-sections -fdata-sections -c foo.c
riscv64-unknown-elf-ld -T linker.ld foo.o -Map=foo.map -o foo.elf

Step 2 — Create a target microarchitecture model for your SiFive core

RocqStat needs a model of pipeline latencies, cache hierarchies and instruction timings. For mainstream SiFive cores (e.g., U74, U84) you’ll often need to build that model yourself from vendor documentation and microbenchmarks. If you’re on a derivative or custom core, collaborate with the silicon team to get exact pipeline timing.

Model components to capture

Pipeline stages: decode, execute, writeback latencies and bypass behavior
Instruction latencies: base cycles per instruction class (ALU, load/store, branch, CSR access)
Memory hierarchy: L1 I/D cache sizes, line sizes, associativity, L2 latency, main memory timing
Speculation and branch prediction: flush penalties and predictor state effects
Vector extension behavior (RVV): microop expansion, strip‑mining costs, tail handling
Hardware accelerators / DMA: bus contention, transfer latency windows

Start with a conservative model (pessimistic latencies) for safety and iteratively refine using measurements.

Step 3 — Run static timing analysis with RocqStat

With the binary and model in place, configure a RocqStat project. The key inputs are the ELF binary, the control‑flow graph (CFG) extracted from the binary/symbols, and the timing model described above.

Typical RocqStat workflow (conceptual)

Import ELF/symbols to build CFG
Associate each instruction or basic block with instruction latency and memory model
Define loop bounds and input constraints (either annotate source or supply annotations file)
Run WCET estimation and inspect path reports

Many organizations use a configuration file (JSON/YAML) that maps instruction classes to cycle counts and defines cache parameters. If VectorCAST integration is available in your environment, export your test cases and map them to RocqStat analyses so reports align with unit tests and requirements traceability.

Step 4 — Validate and refine your model with measurements

Static estimates are only as good as the model. Use hardware counters and trace to validate and tighten bounds.

Options for measurement

CSR cycle counters: RISC‑V exposes mcycle, mcycleh and perfcounter CSRs (mhpmcounter*). On Linux you may need to run in supervisor mode or use a kernel driver to read privileged counters.
perf / perf_event_open: When running Linux on RISC‑V, use perf to capture cycles, cache misses and branch misses as a validation sample set.
Instruction trace / ETM: If your board supports trace, collect instruction traces and align them to RocqStat’s worst‑path suspects to validate path timing.
Cycle‑accurate simulation: Use SiFive’s cycle‑accurate simulator or an FPGA RTL model to generate golden traces when hardware access is limited.

Concrete tip: Implement a small measurement harness that toggles a GPIO or sends a timestamp via UART at entry/exit for critical regions. Read mcycle before and after the region (privileged access) to get raw cycles. Compare median and high‑percentile samples to your static WCET. If hardware shows higher cycles than static worst‑case, investigate missing microarchitecture effects (e.g., bus contention, speculative behavior) and update the model.

Step 5 — Handling RVV and GPU offload (NVLink) gotchas

Embedded AI workloads often use RISC‑V vector extensions (RVV) and, increasingly, external GPUs connected via NVLink. These introduce modeling complexity.

RVV (RISC‑V Vector) specifics

Vector instructions can expand to microops or cause strip‑mining across vector length—capture effective microop count per vector instruction in your model.
Memory bandwidth becomes the limiter: model vector load/store patterns and L1/L2 streaming effects.
Non‑deterministic prefetchers or streaming engines in SiFive cores may make static worst‑case pessimistic—use measurement to refine conservatively.

GPU offload and NVLink concerns

Transfers to GPU introduce asynchronous DMA latencies and bus contention. RocqStat may not model NVLink or GPU runtime internals out of the box.
Strategy: treat offload as syscall‑like black box with measured worst‑case latency, then compose CPU and transfer WCETs conservatively. Where possible, instrument actual transfer-critical paths and include DMA interconnect contention in the memory hierarchy model.
For end‑to‑end embedded AI pipelines, consider hybrid analysis combining RocqStat’s WCET for CPU-only segments and measurement/queueing models for interconnect/GPU segments.

Integration patterns: VectorCAST, CI and regression tracking

Vector’s acquisition of RocqStat points to tighter integration with VectorCAST. While the full product merge may still be rolling out, you can begin practical integration today.

Example integration flow

Use VectorCAST to build and exercise unit tests on the RISC‑V target (or simulator).
Export test artifacts (ELF, map files, coverage data) to a RocqStat project automatically via a script or plugin.
Run RocqStat analyses as a gated CI step—fail the pipeline when WCET increases beyond thresholds or when new unbounded loops are introduced.
Import RocqStat WCET annotations and path reports back into VectorCAST for traceability (requirements → tests → timing evidence).

CI tip: run fast, conservative RocqStat checks on every merge (e.g., function‑level bounds) and schedule deeper whole‑system runs nightly because full WCET with complex caches and RVV can take longer.

Common gotchas and how to avoid them

Missing or stripped symbols: Striping removes essential mapping. Keep symbols and use linker maps for accurate CFG extraction.
Unrealistic compiler optimizations: LTO and aggressive inlining change CFGs. Freeze compiler options for verified builds and document differences between debug and verified builds.
Privileged counter access: On bare metal, you may lack access to mcycle in user mode. Use a small privileged monitor or firmware shim to expose reliable counters for measurement.
Ignoring bus contention: Static single‑core models can understate latency when DMA, GPU transfers, or co‑running tasks contend for memory. Model shared resources conservatively and validate with stress tests.
Model drift: Silicon microarch changes (small uarch revisions) can break models. Automate periodic revalidation with microbenchmarks on actual hardware.

Practical example: analyzing an RVV kernel loop

Let’s walk through a short, practical pattern: a vectorized GEMV kernel offloading some work to RVV.

Build the kernel with -g and avoid inlining so RocqStat reports a clear function boundary.
Annotate loop bounds or provide a loop‑bound file to RocqStat (RocqStat accepts annotations telling the analyzer maximum iterations for variable loops).
Create a timing model for the RVV instruction sequence: assume conservative microop expansion (e.g., 2–4 cycles per microop) and L1 streaming latency for vector loads. Document the assumptions.
Run RocqStat and get an initial WCET bound for the kernel. Identify hot paths and the basic block sequence that dominates time.
Deploy a microbenchmark to the board to execute the same loop under worst‑case data (cache misses) and measure high percentiles with mcycle samples. If measured worst‑case exceeds static estimate, update the RVV load/store latency in your model.

The result is a validated WCET for that kernel and a repeatable process for future kernels.

Scaling this up: whole‑system and heterogeneous pipelines

For embedded AI systems that include several cooperating components (RISC‑V CPU, vector units, GPU via NVLink, and an RTOS), the best practice is a layered approach:

Use RocqStat for CPU and local microarchitecture timing.
Use measured or vendor models for accelerators and interconnects.
Compose timings conservatively with scheduling models (e.g., time‑triggered scheduling or priority ceiling models) to get system WCET.

Case study: what to expect when integrating RocqStat with VectorCAST

"Vector will integrate RocqStat into its VectorCAST toolchain to unify timing analysis and software verification" — Automotive World, Jan 2026

Early adopters reported that adding static timing checks into the unit test pipeline reduced post‑integration surprises: teams caught unexpected control‑flow changes or compiler regressions that increased worst‑case time. Expect an initial investment to build accurate SiFive models, but once in CI, timing regressions become visible like any other test failure.

Advanced strategies and future predictions (2026 and beyond)

Looking ahead in 2026, expect the following trends that affect timing verification on RISC‑V:

Vendor timing model distribution: Silicon vendors like SiFive will increasingly provide pre‑validated timing descriptions or RocqStat model packs for mainstream cores, reducing modeling burden.
Heterogeneous WCET composition: Tools will add first‑class support for composing WCETs across CPU, vector units and GPU domains (NVLink-aware analysis), driven by embedded AI demand.
Cloud‑based cycle‑accurate verification: Expect more cloud services offering cycle‑accurate simulation for RISC‑V SoCs to accelerate model validation without long hardware queues.
Automation in CI: WCET checks will become standard gates for safety‑critical embedded AI stacks—VectorCAST + RocqStat will be a popular path for that automation.

Checklist: getting production‑ready

Keep a reproducible build environment and document compiler/linker flags used for verified builds.
Store RocqStat project files, microarchitecture models and assumptions in version control alongside the codebase.
Automate periodic measurement runs on representative hardware and compare to static estimates.
Create CI gates that fail on unexpected WCET increases and attach path reports for triage.
Engage with your silicon vendor for accurate microarchitecture documentation and validated timing packs.

Final thoughts

Running RocqStat on SiFive RISC‑V platforms is eminently practical in 2026, but it’s a systems engineering effort: good tooling, disciplined build and measurement practices, and careful microarchitecture modeling. The payoff is measurable—fewer surprises, auditable timing evidence and a direct path to combining functional verification with timing safety in VectorCAST integrated pipelines.

Call to action

Ready to bring deterministic timing into your RISC‑V embedded AI stack? Start with a single critical kernel: produce a symbolized ELF, draft a conservative SiFive timing model and run a first RocqStat analysis. If you want a template or a checklist tailored to your SiFive core and toolchain, contact our engineering specialists at pows.cloud for a hands‑on workshop or an audit of your timing verification pipeline.

Unknown

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.