case studyfirmwaretiming

Case Study: Implementing Timing Analysis in a Quantum RTOS for Control Firmware

UUnknown

2026-02-16

10 min read

Hands-on case study: integrating RocqStat-inspired timing analysis into an RTOS for quantum control firmware—testbench, metrics, fixes, and pWCET outcomes.

Hook: Why timing analysis is the missing tool in quantum control firmware

Delivering reliable quantum experiments isn't just about qubits and pulses — it's about deterministic control software that meets microsecond or nanosecond deadlines under complex I/O and DMA traffic. For developers building quantum control stacks on embedded RTOS platforms, the greatest friction is verifying that control firmware will behave correctly under worst-case timing conditions. In 2026, with Vector's acquisition of RocqStat and industry focus on unified timing and verification, integrating rigorous RocqStat-inspired timing analysis into the RTOS development workflow is now practical and essential.

Executive summary — what this case study delivers

This hands-on case study shows how we integrated a RocqStat-inspired timing analysis workflow into an RTOS used for quantum control firmware. You’ll get a reproducible testbench design, instrumentation strategy, measurement-to-analysis pipeline, key metrics, and the concrete outcomes we observed. If you’re a firmware developer, QA engineer, or systems architect, follow the steps below to reduce jitter, validate deadlines, and incorporate statistical WCET (pWCET) estimates into CI.

Why timing analysis matters for quantum control (2026 context)

Quantum control firmware orchestrates pulse generation, feedback loops, readout processing, and calibration — all of which require precise timing. Missed deadlines can corrupt experiments, waste hardware time, or create irreproducible results. In late 2025 and early 2026 the industry accelerated verification investment: Vector’s acquisition of StatInf’s RocqStat signaled that timing analysis and software testing are merging into unified toolchains for safety- and timing-critical domains. For quantum control teams, this trend means better tools are available and integrating such capabilities into RTOS workflows will be expected for production-grade systems.

Industry note: Vector's move to integrate RocqStat reflects a cross-domain need: deterministic timing guarantees will be as important for quantum control as they are today for automotive and aerospace systems.

System overview: hardware, RTOS, and workload

We targeted a representative quantum control stack used in mid-scale lab systems with the following components:

SoC: Xilinx Zynq UltraScale+ (ARM Cortex-A53 + FPGA fabric) — typical for AWG/DAQ hybrid controllers.
FPGA: AWG waveform engines and DMA engines for low-latency transfers.
RTOS: Zephyr 3.x (chosen for active ecosystem, trace hooks, and deterministic scheduling). The techniques apply equally to FreeRTOS or vendor RTOS.
Workload: pulse scheduling task (high priority), demodulation pipeline (medium priority), logging/telemetry (low priority), and fast ISR for ADC/DMA completion.
Measurement host: Linux host connected via JTAG/serial for trace offload; optional ETM tracing with ARM CoreSight for cycle-accurate traces.

Design goals and acceptance criteria

We defined measurable goals up front:

Proof that the pulse scheduler's end-to-end latency is below 5 µs (99.999% of runs).
Reduce ISR jitter to < 500 ns 95% of the time.
Detect and eliminate all deadline misses > 1 µs in a sustained stress test (duration: 24 hours).
Deliver a statistical WCET (pWCET) estimate at probability 1e-6 with 95% confidence for critical tasks.

Instrumentation strategy — what we measured and how

To make timing analysis useful, you need both low-overhead runtime instrumentation and a way to connect dynamic measurements with static analysis. Our instrumentation had three layers:

Hardware cycle counters: On Cortex-A53 we used PMU cycle counters and the DWT counter where available. These provide nanosecond-scale resolution for code sections.
RTOS trace hooks: Zephyr trace events on task switch, ISR enter/exit, and DMA notifications. Events were buffered in a lock-free circular buffer in SRAM and periodically drained to the host.
ETM/CoreSight or FPGA timestamps: For correlating software events to waveform outputs we used ETM/CoreSight or timestamped DMA descriptors in the FPGA that recorded host time at the sample boundary.

Minimal instrumentation example (C, Zephyr RTOS)

// Enable cycle counter (ARM PMU) - safe in privileged mode
static inline void pmu_enable_cycle_counter(void) {
  asm volatile (
    "MCR p15, 0, %0, c9, c12, 0\n" // PMCR: reset and enable
    : : "r"(1));
}

static inline uint64_t read_cycle_counter(void) {
  uint32_t v_low, v_high;
  asm volatile ("MRC p15, 0, %0, c9, c13, 0" : "=r"(v_low));
  // Read high if needed for 64-bit. Implementation depends on core.
  return (uint64_t)v_low;
}

// Instrumented task
void pulse_scheduler(void *arg) {
  while (1) {
    uint64_t t0 = read_cycle_counter();
    schedule_next_pulse();
    uint64_t t1 = read_cycle_counter();
    trace_record_event(EVENT_PULSE_SCHED, t0, t1 - t0);
    k_sleep(K_MICROSECONDS(100));
  }
}

Note: calibrate and subtract the read overhead from measurements.

Testbench and workload generation

We created two complementary test modes:

Deterministic test: Fixed pulse schedule and synthetic ADC interrupts to validate repeatability and baseline WCET.
Stress/randomized test: Randomized background DMA, CPU load, and network I/O to exercise cache effects, branch misprediction, and interrupt storms.

Each run lasted at least 1 hour for deterministic tests and 24 hours for stress tests. We configured the FPGA to inject bursty DMA patterns that mimic real readout bursts during multi-qubit readout operations.

Analysis pipeline — from traces to pWCET

Our analysis pipeline combined measurement-based empirical data with statistical extreme-value techniques, following ideas popularized by RocqStat:

Collect raw latencies and event timestamps from the board and host.
Aggregate per-task and per-ISR execution times; normalize for clock drift.
Apply outlier filtering and overhead subtraction (instrumentation overhead).
Fit the tail of the distribution with a Generalized Pareto Distribution (GPD) using the Peak-over-Threshold method.
Estimate pWCET at target exceedance probability (e.g., 1e-6) and compute confidence intervals via bootstrapping.

Python analysis snippet (conceptual)

import numpy as np
from scipy.stats import genpareto

latencies = np.loadtxt('pulse_sched_latencies.csv')
# choose threshold at 95th percentile
thr = np.percentile(latencies,95)
excess = latencies[latencies > thr] - thr
params = genpareto.fit(excess)
# compute pWCET for target p
p_target = 1e-6
n = len(latencies)
prob_tail = np.sum(latencies > thr) / n
# inverse CDF for GPD
q = genpareto.ppf(1 - p_target/prob_tail, *params)
pwect = thr + q
print('pWCET @',p_target,':',pwect)

Key metrics tracked

We tracked both raw and derived metrics:

Latency: execution time per invocation (mean, median, 99.99th percentile)
Jitter: variance and standard deviation of latency
Deadline miss rate: fraction of invocations exceeding deadline
pWCET: probabilistic WCET at specified exceedance probabilities with confidence intervals
Context switch time: average and worst-case task switch durations
ISR execution times: distribution and tail behavior

Findings and remediation — real results from the case study

After the first measurement pass (deterministic), we found:

The pulse scheduler had median latency 1.2 µs and 99.99th percentile at 9.8 µs — violating the 5 µs target in rare cases.
An ADC/DMA ISR occasionally took up to 12 µs due to cache misses and a non-preemptible third-party driver holding locks.
Context switches peaked at 1.6 µs when switching from the demodulation pipeline to the scheduler under heavy DMA.

Root cause analysis pointed to three actionable problems:

Unbounded work inside an ISR (driver callback performing buffer copies).
Cache eviction patterns caused by background big-data transfers from the DMA engine.
Priority inversion and unprotected shared resources between scheduler and demodulation task.

Remediation steps implemented

Refactor ISR to queue work to a high-priority worker thread and return immediately. ISR now logs a descriptor and triggers task; heavy copies are DMA-driven by the worker.
Enabled cache locking for critical control code regions and pinned scheduler code/data to SRAM to avoid cache-induced jitter.
Applied priority inheritance for the shared resource and reduced shared critical-section durations.
Added prefetching and aligned buffers to reduce unaligned DMA penalties.

Post-fix results and verification

After fixes and a 24-hour stress run under randomized DMA bursts, results improved significantly:

Pulse scheduler median: 1.25 µs (essentially unchanged) — but 99.999% latency dropped from 9.8 µs to 3.9 µs.
ISR worst-case dropped from 12 µs to 2.1 µs. 95% of ISR events were < 600 ns.
Deadline miss rate for the 5 µs deadline dropped to 0 over the 24-hour run at measured probability resolution (no observed misses).
Statistical pWCET at 1e-6 (estimated via GPD fitting) was 4.2 µs with 95% bootstrap confidence interval [3.7 µs, 5.1 µs].

These outcomes mean we achieved both practical determinism and a quantifiable pWCET bound that one can feed into scheduling and acceptance tests.

Tradeoffs and considerations

Instrumenting and hardening the RTOS has costs:

Instrumentation overhead — cycle counter reads and trace buffering add tiny overhead. We subtracted measured overhead in analysis but keep instrumentation in CI to detect regressions.
Memory tradeoffs — pinning code and locking caches consumes fast SRAM; this increases cost on constrained controllers.
Statistical pWCET depends on representative workload and sufficient sample size. For extremely low exceedance probabilities (e.g., 1e-9) you'd need impractically long runs or rely on combined static+statistical approaches.

Advanced strategies for production-grade timing safety

For teams adopting this workflow into production, consider:

Hybrid analysis: combine static WCET tools with measurement-based statistical methods (RocqStat-like) to bound rare pathologies that measurements alone may miss. See our notes on hybrid approaches to data and analysis.
Continuous timing regression testing: include nightly stress runs and pWCET estimation in CI; fail builds when pWCET increases beyond a threshold.
Formalize timing SLAs: convert pWCET outputs and deadline miss rates into SLAs for test benches and experiment orchestration layers.
Trace correlation: link software traces to FPGA/DAQ timestamps so software timing anomalies can be correlated to hardware events (crosstalk, bus contention). For low-latency trace correlation patterns, see edge low-latency strategies.

Practical checklist: integrating timing analysis into your RTOS workflow

Identify safety- or timing-critical tasks and ISRs in your control firmware.
Add low-overhead cycle counters and RTOS trace hooks; calibrate overhead.
Design deterministic and randomized stress workloads that mirror production I/O and DMA patterns.
Automate data collection and implement a measurement-to-analysis pipeline (Python/CI jobs) that outputs pWCET with confidence intervals.
Remediate root causes (refactor ISRs, cache/pin critical regions, fix priority inversions).
Incorporate nightly/PR timing regressions into CI and block merges on pWCET regressions.
Document timing assumptions and publish pWCET numbers alongside firmware releases using a lightweight docs platform (see docs hosting options).

Outlook: trends and predictions for 2026 and beyond

With vendors like Vector moving to integrate timing analysis technologies and increased cross-domain interest, expect the following in 2026–2027:

Unified toolchains that couple unit testing, static verification, and probabilistic timing analysis (ease of adoption for embedded quantum stacks).
Cloud-based timing labs where teams can run standardized timing stress tests on shared hardware to reproduce rare events without owning large fleets of controllers. (See recent advances in cloud data and sharding tooling at Mongoose.Cloud.)
Standard timing metrics for quantum control firmware (pWCET at defined probabilities, deadline breach rates) to enable procurement and interoperability.
Tighter hardware‑software co-design: AWG vendors will expose richer timestamping and trace channels to support precise timing verification.

Actionable takeaways

Start lightweight: instrument the top 2–3 critical tasks and run short deterministic tests to establish a baseline.
Adopt a statistical tail-fitting approach (GPD/Peak-over-Threshold) for practical pWCET estimation; don’t rely only on mean/median.
Automate timing checks into CI — timing regressions are as important as functional regressions.
Correlate software timing with hardware traces for true root-cause analysis.

Final thoughts and next steps

Integrating a RocqStat-inspired timing analysis workflow into your RTOS for quantum control firmware turns timing assumptions into measurable, auditable artifacts. In our case study the combination of light-weight instrumentation, staged remediation, and statistical analysis converted rare deadline violations into quantified pWCET guarantees suitable for CI and acceptance testing. As tooling converges in 2026, teams that build these capabilities now will gain reliability, reproducibility, and a measurable path to operational timing safety. For guidance on creating auditable trails and documentation, see designing audit trails.

Call to action

Ready to try this in your stack? Clone our baseline testbench (FPGA stubs, Zephyr hooks, and Python analysis scripts) from the shared repo, or request a timing audit. If you want a quick win, start by instrumenting your top-priority ISR and run a 24-hour stress test — then apply the GPD tail-fit to generate a pWCET you can ship with your firmware.

Get involved: share your results, join our developer community, or book an audit to convert timing risk into measurable SLAs for your quantum control firmware.

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