AI × HARDWARE ENGINEERING
SiliconPilot helps hardware teams spend less time on repetitive work—from turning specifications into tests and debugging regressions to managing physical design runs. Work within your existing simulators, infrastructure, and methodology.
01 / OUR EXPERTISE
Focused engineering engagements that connect AI to the tools, constraints, and artifacts your team already works with.
Bring structure to iterative PnR work: block tracking, recipe curation, floorplan checks, run orchestration, and report-driven review.
Turn specifications into tests, debug regressions, and close coverage gaps with assistants built around your existing simulators and verification methodology.
Connect specifications, scripts, reports, and domain knowledge into a practical assistant built around your team's bottleneck.
PHYSICAL IMPLEMENTATION / DEEP DIVE
A physical implementation copilot that coordinates agents, Innovus runs, and engineering evidence across floorplanning, pre-CTS, CTS, routing, and closure.
Start with your netlist, libraries, technology files, MMMC setup, timing constraints, and floorplanning methodology. The copilot prepares versioned recipes, launches bounded runs in your EDA environment, reads the resulting reports, and chooses the next permitted action against agreed stage gates.
Each iteration has a reason: retry the current stage with a targeted change, advance from an accepted checkpoint, or return to an earlier stage when the root cause demands it. Engineering review controls changes to design intent and exceptions.
REPORTS → DECISIONS
Acceptance thresholds come from your methodology. The copilot checks timing by mode and corner alongside congestion, placement legality, clock quality, and physical violations. A better WNS alone is insufficient if the change creates new failures elsewhere.
| Evidence | Next action | Required check |
|---|---|---|
| Pre-CTS setup misses its stage target; congestion remains acceptable | Try a bounded placement / optimization recipe with a tighter internal timing target | Compare WNS, TNS, violating endpoints, area and congestion; preserve the functional SDC |
| Timing improves but routing hotspots grow | Reject the regression; adjust density, placement or the floorplan | Check routability before spending on CTS and detailed routing |
| CTS introduces hold failures or poor skew | Inspect clock topology and buffering; rerun CTS or its optimization | Verify setup and hold across configured modes and corners |
| Post-route violations cluster around a macro channel | Attempt localized repair; return to placement or floorplan if structural | Recheck connectivity, DRC and timing after each candidate |
| Stage gates pass | Advance using the accepted checkpoint | Archive the recipe, reports, metrics and decision rationale |
| Run fails, progress stalls, or iteration budget is exhausted | Stop and escalate with a reproducible failure bundle | Retain the best accepted checkpoint and summarize unresolved issues |
Track block, stage, run status, dependencies and retry budgets. Launch permitted jobs, distinguish tool errors from design failures, and resume from known checkpoints.
Apply approved floorplan, placement, clock and routing actions through versioned Tcl recipes. Keep each experiment scoped so its effect can be explained.
Parse timing and physical reports, compare candidates, identify regressions, and recommend advancement or rollback. Engineers approve methodology exceptions.
ENGINEERING FOUNDATION
The approach draws on prior development of a Python multi-agent physical implementation flow: specialized agents generate Tcl for floorplanning, pre-CTS, CTS, post-CTS and routing, while an orchestrator manages dependencies, execution, reports and logging. Pass / fail evidence drives the handoff between stages.
Prior report-analysis work processed CSV timing data, classified path types, examined register-to-register violators and worst paths, and produced structured JSON context. Exploratory clustering, slack regression and correlation analysis helped investigate patterns; stage acceptance remains tied to EDA reports and agreed gates.
Copilot CLI transition work defines regular tracking of blocks, curated recipes, floorplanning compliance, experiments and related artifacts. Innovus / Cerebrus integration can be scoped into the engagement, with explicit objectives, baseline comparisons and evidence reviewed before methodology promotion.
CLOSURE / HANDOFF
The target is a saved implementation database with zero reported violations under the agreed Innovus DRC checks, plus accepted timing and connectivity results. Repeated Innovus sessions perform scoped repairs and recheck the design until the gates pass or a documented stopping condition is reached.
Deliverables depend on the block, constraints and tool setup. Innovus DRC closure is one checkpoint; final signoff also requires the project's qualified physical verification, extraction, timing and power integrity checks.
Start with one block and an agreed baseline. Evaluate the copilot on closure quality, reproducibility, turnaround time and engineer effort.
Scope a physical implementation pilot ↗VERIFICATION / TWO CONNECTED THREADS
UVM block verification and software-driven SoC verification: two workflows with distinct stimulus, shared traceability, and evidence from real simulator runs.
The copilot turns specifications into a reviewable verification plan, maps requirements to stimulus and checkers, and coordinates generation, compilation, simulation and regression analysis. Each result links back to the requirement, test, configuration and run that produced it.
Start with one block or one SoC interaction. Engineers review ambiguous intent, legal constraints and expected behavior before expanding the test space. Passing generated tests supports the agreed verification scope; completeness must be assessed against the plan and coverage.
THREAD 01 / UVM
A scoreboard checks observed behavior against an independently reviewed expectation. Generating stimulus and its expected answer from the same unreviewed assumption can hide bugs.
THREAD 02 / SOC
Knowledge-graph traversal organizes scenario selection and dependencies. Simulator observations and data comparisons establish whether the scenario passes.
SOC EXAMPLE / MULTI-HOP DATA INTEGRITY
Illustrative sequence: the Cortex-M fills Bank A with a known pattern, starts the A-to-B transfer, checks completion and verifies Bank B. It then starts B-to-C, verifies Bank C, writes the verified payload back to Bank A and checks it again. Every hop uses a bounded wait, explicit status checks and recorded mismatch offsets. Cache maintenance and memory barriers are included where the target memory attributes require them.
Reachable banks and DMA channels; prerequisite register writes; legal width / burst / length combinations; completion and interrupt dependencies; links from each path to its requirements and test history.
End-to-end data integrity, transfer status, timeout behavior and interrupt expectations. Bus-level checks can add protocol, arbitration and ordering evidence where the environment provides suitable observers.
RESULTS → NEXT ACTION
| Evidence | Copilot action | Acceptance gate |
|---|---|---|
| Specification leaves expected behavior ambiguous | Flag the requirement and request engineering resolution | Reviewed expectation before generating its checker |
| Generated UVM or C test fails to compile | Correct scaffolding, dependencies or BSP integration; rerun | Clean compile and reproducible invocation |
| Scoreboard mismatch or DMA readback failure | Preserve seed / path / configuration; localize and reproduce | Classify DUT, testbench, model or configuration issue; retain the original evidence |
| A reachable path or vPlan coverage bin remains untested | Generate a targeted legal scenario and check its mapping | Observed coverage and passing checks, or a reviewed exclusion |
| Reviewed verification gates pass | Package the regression and traceability bundle | Agreed coverage targets, resolved failures and documented exceptions |
Structure the vPlan, graph topology, legal combinations and dependencies. Keep source references and unresolved assumptions visible.
Create UVM components, sequences and C scenarios; compile, launch bounded regressions and record tool versions, seeds and configurations.
Analyze mismatches, logs and coverage; propose targeted next tests and preserve failure evidence. Engineers review changes to expected behavior and exclusions.
ENGINEERING FOUNDATION
The approach draws on prior multi-agent UVM testbench-generation work with integrated reference models, and C-based test expansion spanning multiple memory-bank configurations, DMA source / destination widths, burst lengths and transfer sizes. Knowledge-graph-guided sequencing connects legal combinations, prerequisites and test history to the next scenario.
Those methods guide the offering. Each customer pilot validates generated artifacts against its own RTL, register definitions, simulator environment and reviewed verification objectives.
VERIFICATION / HANDOFF
Deliver a reproducible verification workflow and evidence for the agreed scope. Evaluate test correctness, requirement mapping, coverage improvement, reproducibility and engineer effort against an existing baseline.
Choose a UVM block or a Cortex-M / DMA / memory interaction as the first pilot, with explicit checks and acceptance criteria.
Scope a verification pilot ↗02 / THE APPROACH
A bounded pilot gives your team a concrete result to evaluate before expanding to a broader methodology.
Define your first pilot ↗Identify the workflow, available artifacts, tool boundaries, and the decisions that need an engineer.
Connect the copilot to agreed tools and data. Make recommendations and execution paths reviewable.
Compare engineering effort, output quality, and repeatability using an agreed evaluation set.
Deliver the implementation, documented recipes, evaluation results, and a roadmap for the next iteration.
03 / INSIDE THE WORKFLOW
An illustrative view of how a copilot can organize engineering work. Select a workflow to explore its inputs and deliverables.
04 / ENGINEER TO ENGINEER
Founded by Aayush, a design engineer working on AI accelerators, physical design, and AI-assisted hardware workflows.
The practice brings together hands-on PnR experience and work on verification assistants, testbench expansion, knowledge graphs, and methodology orchestration. The focus is practical: useful context, controlled execution, and results an engineer can inspect.
05 / LET'S BUILD
Tell us about one workflow worth improving. Start with a scoped pilot, a measurable baseline, and a clear engineering owner.