Close the block
Writes RTL, testbenches, assertions and fixes until lint, simulation, and timing tools pass them.
- Judge
- Verilator · VCS · STA
- Cycle
- Minutes
The Silicon Intelligence Platform · In-Situ ArchitectureSheet 01 / 04 — General arrangement
Autonomous
Silicon Intelligence.
Building the recursive self-improvement infrastructure for the next Moore’s Law.
Moore’s law is no longer bottlenecked by physics. It is bottlenecked by human engineering bandwidth. We build the sovereign intelligence layer for semiconductor engineering—turning enterprise RTL archives, proprietary PDKs, and verification history into compounding autonomous capability, scored by deterministic EDA verification, and kept strictly inside your firewall.
In-situ parameters0 B egress · air-gapped
Deterministic judgeVerilator · VCS · STA slack
Mathematical traceabilityAuditable formal proof
Sheet 02 / 04 — Strategic RealityThe Engineering Dilemma
Three reasons generic AI fails
in semiconductor design.
A bug in silicon is not a patch—it is a $50M respin. When generic models hallucinate timing, burn runaway tokens, or leak golden RTL, they break engineering velocity and destroy your moat.
Design complexity (Transistor & protocol matrix)Engineering bandwidth
01 — Zero Tolerance
Looks like Verilog ≠ Meets timing
In software, a bug is patched in minutes. One clock glitch, CDC hazard, or timing violation in silicon means a $50M+ respin and 9 lost months. Hardware engineering has zero tolerance for probabilistic guessing.
1 Glitch · $50M Respin · 9 Mo Lost02 — Sovereign Moat
Commodity models erase your edge
Your competitive moat is decades of golden RTL, custom PDKs, and design decisions. Public models leak IP and give competitors the exact same answers. An AI that learns your workflows turns your private assets into compounding velocity.
Internal IP → Compounding Moat03 — Cost Trap
Runaway tokens, fragile results
Brute-force RAG with 100k+ token prompts burns $10–$200/day/eng and still yields single-digit first-pass compile rates. Unpredictable token spend prevents leadership from deploying AI across hundreds of engineering seats.
Unpredictable Spend Stalls AdoptionSheet 03 / 04 — Customer ImpactFour Measurable Outcomes
Four outcomes your engineering
leadership can measure.
Not vague productivity promises—concrete, quantifiable metrics benchmarked directly against your current design cycles and fixed compute budgets.
01 — Velocity
40–65%
Shorter iteration turnaround
RTL from specs and legacy blocks in house style. From “bug found in regression” to “verified fix” in minutes, not days.
02 — Grounding
>95%
Alignment with internal guidelines
Learns how your team designs: naming conventions, register layouts, clocking lore, and standard cell preferences.
03 — Economics
60–80%
Lower token cost per task
Task-optimized models enable 5× more work on a fixed compute budget. Affordable for every engineering seat.
04 — Control
Zero
IP leakage, zero AI-ops burden
100% inside your firewall on on-prem GPUs. No 20-person AI platform team to hire. You own all adapters and eval criteria.
Where we start · Four high-ROI entry workflows
Start with bounded workflows that automated testbenches and EDA tools can grade objectively:
Workflow A · Spec-to-RTLSubsystem & Bridge Design
Translates register specs, timing diagrams, and FSMs into synthesizable SystemVerilog in your house style.
85%+ clean syntax · AXI / PCIe / DMAWorkflow B · VerificationUVM Testbench & SVA
Analyzes RTL and interface protocol rules to generate corner-case SVA assertions and constrained-random sequences.
+30% coverage closure · frees senior DVWorkflow C · DebugRegression Triage & Root Cause
Ingests failure logs, assertion triggers, and VCD wave slices to pinpoint root causes and propose verifiable RTL fixes.
5× faster triage · minutes, not daysWorkflow D · Sign-offTiming Closure & ECO Advice
Reads STA reports and negative slack on critical paths to recommend structural restructuring and fanout splits.
2× fewer ECO iterationsPlatform ArchitectureThe Recursive Self-Improvement Engine
One loop.
Three scales.
The same propose → verify → learn cycle, nested across three scales. Each loop hands what it learns to the one outside it.
Close the flow
A failure caught at physical signoff changes how RTL gets written upstream. Stages teach each other.
- Judge
- Downstream results
- Cycle
- Days
Close the company
Every tape-out, respin, and bug patch becomes sovereign weights. The next program starts ahead.
- Judge
- Silicon bring-up & ATE
- Cycle
- Every tape-out
Sheet 04 / 04 — EvidenceHardware Ground Truth
Scored by physics.
Validated on silicon.
Zero marketing fluff. We validate capability against your senior engineer’s archived solution—and on physical FPGA hardware.
Historical Blinded BenchmarkZero-Risk Pilot
Graded against your answer key
Provide the specs and regression tests from a completed tape-out. Our model runs blind—without seeing the solution—and is scored directly against your senior engineer’s archived golden fix.
IP RiskZero (Archived tape-out)
Answer KeySenior engineer fix on file
ExecutionBlind in-situ run
GradingAuditable A/B report
Hardware Sign-OffPhysical Silicon Proof
Real FPGA Linux cold boot
An autonomous RISC-V core with MMU synthesized through closed-loop RL, mapped to physical FPGA fabric—successfully cold-booting the Linux kernel to serial prompt.
TargetRISC-V with MMU
MediumPhysical FPGA board
VerificationLinux boots 100%
Sign-offZero glitches / Clean STA
Our benchmark · chipbench.aichipbench.ai ↗
ChipBench
The open leaderboard for AI chip design. Agents optimize real designs and build processors, scored strictly by what EDA tools report.
84%EvolvingLab SOTA
38%Claude 3.5
29%GPT-4o
76Scored runs
RSI Research Foundations · The Science of Silicon Intelligence
- RSI TheoryToward Genuine Recursive Self-Improvement in Silicon SystemsHow autonomous agents convert closed-loop EDA simulation feedback into compounding model capability without human data labeling.Read ↗
- AuditabilityDeterministic Hardware Artifacts & Traceable Decision TreesEvery synthesized RTL block, assertion sequence, and bug fix is 100% reproducible, step-traceable, and auditable by senior architects.Read ↗
- ICLR 2026Autonomous Multi-Step Goal Completion Across Hardware WorkflowsRigorous evaluation of whether autonomous agents can drive complex, multi-day chip design and verification cycles to verifiable sign-off.Read ↗
- EDA HarnessClosed-Loop Experimentation with Deterministic Physics JudgesGrounding model reinforcement in objective EDA tool feedback: Verilator logic simulation, SVA assertions, and STA timing slack.Read ↗
Four inputs from you.
Everything else is on us.
Start with a 30-minute technical discovery. No upfront budget. Real historical silicon. Hard numbers.
What we need from you
- One concrete use caseA specific controller RTL block or a UVM coverage bottleneck.
- One historical datasetA completed project taped out 3–5 years ago with known answers.
- Verifiable acceptance criteriaYour existing lint commands, regression scripts, and timing targets.
- Two points of contactOne senior DV/RTL technical lead and one engineering director.
What you don’t need to do
- No AI platform team to hireZero need for dedicated ML ops, fine-tuning, or infra engineers.
- No agent frameworks to maintainWe deliver a production engineering system, not a raw API key.
- No active crown jewels exposedPoV uses old tape-outs; production runs 100% inside your air-gap.
- No budget riskPaid PoV credited 100% against platform deployment.