TL;DR
Standard vector RAG fails in IC design because silicon is a graph, not a list of documents. The Arango Contextual Data Platform fuses graph traversal, vector search, and document storage in a single engine — replacing fragmented stacks with one deterministic pipeline. The result: 92% fewer LLM tokens, 5x lower query latency, zero hallucinated module links, and automated spec-to-silicon traceability across multi-repo hardware datasets.
The Problem
Vector RAG is flying blind in silicon design
A System-on-Chip is a deeply nested hierarchy — not a flat list of documents. Standard vector RAG matches on semantic similarity, so ‘clock signal’ in an ISA spec looks identical to ‘clock signal’ in a UART block. If the AI retrieves the wrong RTL (Register Transfer Level) module, the LLM fabricates a connection, and a verification engineer loses hours on a requirement that never existed.
Fragmented Graph DB + Vector DB stacks compound the problem: three sequential network hops per query compounds to 300ms+ at 50,000-node scale. Unacceptable in an interactive design loop.
The Solution
One engine. Graph, vector, and document — unified.
Within the Arango Contextual Data Platform, ArangoDB executes graph traversal and vector similarity in a single AQL query, in the same C++ execution context. No application-side join. No format serialization. No pipeline to maintain.
The architecture runs a four-stage deterministic pipeline — PDF ingestion, entity classification, entity resolution, and type-safe semantic bridging — converting unstructured hardware specs into a structured, auditable Knowledge Graph. A Type-Compatibility Matrix prevents architecturally nonsensical links regardless of semantic similarity score.
The graph also operates across time. Design Epochs cluster Git history into named periods — initial commits, major refactors, milestone releases — so engineers can query exactly what a subsystem looked like at any point in its history. Design Situations automatically detect pivotal moments across all five IC repos — subsystem additions, major refactors, release prep — and surface similar historical patterns across designs, preventing teams from repeating past architectural mistakes.
The Results
Measured on 50,000 nodes · 180,000 edges · 5 open-source IC design repos
- 92% fewer LLM tokens: 460 tokens retrieved vs 5,200 in LangChain + FAISS — with 91% precision vs 7%.
- 5x lower latency: Sub-60ms query time vs 300ms+ in a fragmented Graph DB + Vector DB stack.
- Zero hallucinated module links: Type-safe semantic bridging replaces stochastic retrieval with deterministic paths.
- Cross-repo lineage — new capability: 4-hop AQL traced OR1200 debug specs from 2001 to ibex_core.v in modern RISC-V. Knowledge previously locked in senior architects’ heads.
- Cross-repo design situation matching: Automatically identifies similar historical design moments across all five IC repos — so your team doesn’t solve the same problem twice.
- Temporal time-travel queries: Design Epochs let engineers query exactly what any subsystem looked like at a specific milestone release — across any of the five repos.
- Organizational risk surfaced automatically: Bus Factor mapping identifies single-point-of-failure IP blocks without manual dependency reviews.
Read More
This is a summary. The full article has the depth.
The Medium deep-dive covers full AQL query walkthroughs with line-by-line explanations, the complete graph schema taxonomy, the Type-Compatibility Matrix design, the OR1200-to-IBEX lineage trace step by step, Design Epoch and Design Situation architecture, honest trade-off analysis, and benchmark data comparing ArangoDB against LangChain + FAISS and Neo4j + Pinecone.
The GitHub repository has every pipeline stage ready to run against your own RTL repositories.
Read the full technical deep-dive on Medium
Full AQL walkthroughs, graph schema, OR1200-to-IBEX lineage trace, temporal architecture, and benchmark data.
