Tattvam AI offers an AI‑driven optimization layer that automatically evaluates power, performance, and area trade‑offs in semiconductor designs using a first‑principles reasoning engine. Integrated via standard APIs with leading EDA suites, it provides real‑time, explainable design configurations that reduce manual iteration cycles for ASIC design teams.
Funding
Funding not disclosed
Founders
Product
Problem
Designing modern integrated circuits requires evaluating millions of interdependent trade‑offs across power, performance, and area. These decisions are currently distributed among hundreds of engineers, leading to extensive manual iteration and long design cycles. Late‑stage changes propagate throughout the design hierarchy, causing costly delays and rework.
Solution
Tattvam AI delivers an intelligence layer that embeds a first‑principles reasoning engine into the semiconductor design flow. The platform models design intent and physical constraints to autonomously resolve the majority of power‑performance‑area decisions, reserving human expertise for only the most critical interventions. By generating optimized design configurations in real time, it shortens iteration loops and reduces the engineering hours required for tape‑out preparation. The system integrates with existing EDA toolchains via standard APIs, allowing seamless data exchange and preserving legacy workflows. Results are presented through an explainable dashboard that traces each AI‑driven decision back to its underlying constraints, enabling designers to validate and trust the outcomes.
Target Audience
The primary customers are ASIC design teams, fabless semiconductor companies, and chip design architects who need to accelerate complex silicon projects while maintaining rigorous performance and power targets.
Features
- First‑principles AI engine that encodes semiconductor physics and architectural constraints to perform holistic trade‑off analysis
- Automatic optimization of power, performance, and area across hierarchical design blocks with constraint propagation
- Plug‑in adapters for leading EDA suites (Synopsys, Cadence, Mentor) supporting native file formats (e.g., LEF/DEF, Verilog)
- Explainable AI reports that map each recommendation to source constraints and design intent for auditability
- Cloud‑native compute backend with on‑demand scaling to handle large design spaces without local hardware investment
- RESTful API and SDK for integration into custom design automation pipelines and CI/CD environments
- Role‑based access control and end‑to‑end encryption to protect IP throughout the optimization workflow