Verification for AI-based embedded development
The independent verification layer for AI-based embedded development: live processor trace, source-attributed execution, acceptance outside the generator.
AI can generate embedded software at machine speed. Accemic provides the independent verification layer that turns execution on real hardware into trustworthy, machine-readable evidence for the next iteration.
The generator proposes. The real target provides the evidence. Acceptance stays outside the generator.
What Accemic brings to the loop
Verification, not generation. We work on the independent verification side of AI-based embedded development. Generation can change rapidly; the criteria and the evidence used to judge the result do not sit inside it.
Processor trace is our domain. We have been building processor-trace technology for over twenty years. CEDARtools® turns it into structured feedback that software can consume directly.
Object code, attributed to source. The processor executes optimized object code. Engineers define intended behavior in requirements and review tests and structural obligations through source-level artifacts. Accemic makes the relationship between the executed object code and those reviewable artifacts explicit and verifiable, including cases where compiler optimization removes source-level distinctions.
These capabilities enable CEDARtools.Forge. Live trace processing, reliable object-to-source attribution and independent acceptance are the technology foundation it builds on.
Why conventional workflows do not close the loop
Existing embedded tools were predominantly designed to support engineers, test campaigns or assurance activities. AI-based development introduces another requirement: verification results have to become trustworthy, machine-readable feedback for the next automated iteration.
That requires three properties at the same time: reliable source attribution, controlled observation of the executable under test, and a live machine-readable result. Miss one and the loop stalls, misleads, or waits for a person.
Object code to source
Feedback attributed to the wrong construct is worse than none - it steers the next iteration into the wrong place, confidently.
Passive where the distinction survives optimization. CEDARtools.Anchor where it does not. Unresolved where neither applies - and for a loop that third case is the one that matters. It can act on “unresolved here, and this is why”. It cannot act on a percentage that averaged the awkward parts away.
Reliable object-to-source mapping
Evidence from the executable under test
Not zero intrusiveness - chosen intrusiveness: set for the question being asked, bounded, and measured into the record, instead of fixed by the method and argued away afterwards.
Passive trace adds nothing to the application, so the measured executable can be the optimized executable under test. Where an obligation needs a distinction the optimizer removed, Anchor* supplements it deliberately, and still without writing coverage state from the application. Either way the perturbation travels with the result.
The measurement effect is part of the evidence, not a footnote to it.
Live, and machine-readable
We do not merely record trace for later analysis. We process execution continuously while the embedded target keeps running. CEDARbox reconstructs control flow and aggregates metrics in FPGA fabric at line rate. Duration is governed by the verified sustained-processing envelope, not by the capacity of a capture buffer.
What comes out carries executable identity, mapping basis, configuration, observation scope, validity events and unresolved obligations. A reviewer can inspect it, a partner tool can import it, a loop can branch on it. A record travels through a toolchain; a screenshot does not.
Live trace processing · Measurement records
Built to integrate with the AI development stack
Accemic is not building another code generator. We provide the independent verification layer around generation. Generation and acceptance must not collapse into the same mechanism: the generator may propose or modify the implementation, while acceptance rests on independently defined criteria and on evidence obtained from execution on the real target.
That layer can connect AI development platforms and firmware agents with compilers, IDEs, HIL systems, requirements environments, test tools, static analyzers and timing tools. The generator remains replaceable. The verification boundary remains independent.
For AI-development and embedded-tool vendors the integration boundary is straightforward: generate with your stack, execute on the real target, verify against independent runtime evidence.
This is where we see Accemic in the future embedded-development stack.
Build the embedded verification loop with us
From verification technology to a development method
CEDARtools.Forge builds on these verification technologies to connect generation, execution, evidence and acceptance into one controlled development process.
Requirements define what has to be achieved. AI can generate and regenerate the implementation. The real target provides the evidence. An independent gate determines whether the recorded acceptance criteria have been met.
We believe this separation between generation and verification is fundamental to scalable AI-based embedded development. Forge* is how we are turning that principle into a practical development method for embedded systems; it is currently offered as a guided pilot, not as a generally supported product configuration.
Against the tools you own
Prospects ask this by name. Four tool families come up - three established ones and, increasingly, AI firmware agents that read the datasheet, write the code, flash the board and run their own tests. The answer is the same for all four: they are very good at what they were built for. The established three were built for a human in the loop - a session to navigate, a report to read, a package to review. An agent closes the functional loop: it tests what it wrote, on the board, and the code runs. Whether the delivered binary did what was required is a different loop - the acceptance loop. The table answers by design center rather than by vendor, because capabilities vary by product, version and configuration; it describes the design centers as we meet them and the Accemic target architecture, not a universal statement about any specific competing product.
| Tool family | Optimized for | Attribution | Intrusiveness | Result |
|---|---|---|---|---|
| Interactive debug and trace probes | an expert at the target | object-level; source attribution under optimization stays expert judgment | passive on the trace port | a session to inspect |
| Instrumentation-based test suites | a managed test campaign | source-level by construction, on the instrumented build | fixed by the method; the measured build may differ from the delivered one | a report after the run |
| Timing and coverage evidence suites | a defensible assurance package | via instrumentation points or trace, per configuration | configurable with the method, not per observation | an artifact after the campaign |
| AI firmware agents with their own test loop | a developer at the bench who needs working firmware quickly | to the datasheet, for what was written; to the agent’s own tests and logs, for what ran | the agent’s test and log code are part of the build under test; the delivered build may differ | code that runs and passes the agent’s own tests; a log that feeds the next iteration |
| Accemic CEDARtools | a loop that closes without a person in the latency path | passive, Anchor*, or unresolved - stated explicitly | per observation task, bounded, measured into the record | a record of the delivered binary, at line rate while the target runs |
Every row above the last is a tool doing its job well. The last row is a different job.
Where to go next
Explore CEDARtools.Forge · Discuss integration, licensing or a strategic fit