R&D and operations are disconnected
Legacy ERP environments remain blind to the compute load, capital burn, and rapidly changing execution states inside EDA flows, creating management blind spots across schedules and budgets.
Vision
The operating system thesis
The source document defines the platform as the world’s first AI-Native Semiconductor Operating System and frames its central mission as breaking the siloes between chip design, enterprise management, and supply chain logistics through autonomous generative AI.
EDA + ERPFoundational semantic integration rather than loose tool stitching.
Agentic AIAutonomous agent swarms communicate through message queues.
MicroservicesIndependent expert modules scale by stacking hardware nodes.
Closed loopBusiness signals and engineering workloads continuously inform each other.
Pain points
Why legacy enterprises break
The whitepaper identifies three structural problems: ERP systems that cannot see EDA consumption, premium license pools that remain underutilized or poorly scheduled, and institutional knowledge that disappears when engineers leave.
EDA licenses are economically inefficient
High-value EDA licenses sit idle during some windows and become bottlenecks during urgent project phases because they are not dynamically reallocated across projects.
Engineering learning does not compound
Tape-out lessons, bug histories, and debug know-how often stay in personal memory rather than in a reusable enterprise knowledge layer.
Architecture
The four-brain system
The supplied PDF organizes the joint architecture into four domain brains connected by asynchronous microservices and high-throughput message queues.
Engineering brain
Handles semiconductor engineering knowledge through specification ingestion, synthesis-ready RTL generation, and autonomous verification and root-cause isolation.
Execution brain
Runs 24/7 orchestration across physical design loops from P&R to signoff and targets maximal license utilization through autonomous job submission, monitoring, ECO, and rerun cycles.
Business brain
Connects engineering resource usage into ERP-facing ROI dashboards, milestone forecasting, and strategic planning through virtual executive agents.
Manufacturing brain
Interfaces with MES and factory telemetry to convert yield and packaging variation into upstream design-for-manufacturability feedback.
Closed loop
ERP and EDA as one control system
The proprietary core of the proposal is a bidirectional loop in which engineering activity changes enterprise economics instantly, while business priorities push back into design execution.
1
Real-time cost and compute feedback
When EDA workloads trigger heavy simulation or signoff routing, the system projects license cost, infrastructure draw, and milestone impact into the ERP ledger through a semantic translation layer.
2
Operational strategy ingestion
When ERP captures supply shifts or urgent client priorities, those signals feed back into the EDA scheduler so the system can reclaim and shift expensive license capacity.
3
Enterprise-wide optimization target
The document expresses EDA economics through the formula CostEDA = L × C × U and explicitly states the objective of driving utilization U toward 1.
Deployment roadmap
Scalable implementation tiers
The roadmap presents four deployment levels spanning growth-stage design houses to foundry or IDM-scale organizations, with corresponding AI infrastructure, storage, and integration models.
| Level | Target organization | Hardware profile | ERP + EDA feature focus |
|---|---|---|---|
| MicroOS (Level D) | 100 to 500 employees, growth-stage design house | 1 to 2 NVIDIA H100/H200 8-GPU servers, 2 L40S workstations, 1.5TB RAM, 100TB NVMe vector database | Smart license allocation with dynamic shifting around ERP deadlines and burst compute windows |
| Horizon (Level A) | 500 to 2,000 employees, mid-to-large design house | 2 to 4 H100/H200 or B200 clusters, 2TB RAM, 500TB all-flash NVMe, 400Gbps InfiniBand | Dynamic license throttle across departments when enterprise priorities change |
| CoreOS (Level B) | 5,000 to 20,000 employees, multidivisional global giant | NVIDIA SuperPOD with 32 to 64 B200 nodes, 10PB+ distributed storage, cross-region SD-WAN | Global 24/7 handover with regional ERP cost allocation and continuous log processing |
| SiliconIntelligence (Level C) | 20,000 to 100,000+ employees, foundry or IDM eco-leader | 128 to 256 NVIDIA Blackwell GB200 superclusters, IGX edge AI enclosures, 100PB+ industrial data lake | End-to-end quad loop linking factory yield, EDA retuning, financial impact modeling, and material adjustment |
Governance
Security and human control
The document closes with a three-layer governance model designed to protect intellectual property, isolate tenants, and preserve human authority over critical design and financial actions.
On-premises seclusion
Core LLM execution and vector knowledge bases are intended to run air-gapped or on private infrastructure with no outbound telemetry to public APIs.
Granular RBAC
Multi-tenant access controls are designed so agents cannot cross-query protected IP between product divisions.
Human-in-the-loop imperative
The workflow follows “AI Proposes. Human Approves.”, with hard approval gates for ECO actions, tape-out signoff, and absolute ERP spending approvals.
The strongest message in the paper is not simply automation. It is enterprise coordination: financial systems, design flows, and manufacturing telemetry acting as one continuously learning semiconductor operating layer.