XORVIS AI provides an AI‑native platform that automates semiconductor design from architecture exploration through post‑silicon validation. It uses predictive models to generate RTL code, create verification tests, and optimize physical‑design parameters, reducing time‑to‑market and improving power‑performance‑area targets. The cloud‑based workspace enables collaborative, version‑controlled design with seamless integration to major EDA toolchains.
Funding
Funding not disclosed
Founders
Product
Problem
Semiconductor design cycles are dominated by manual, iterative workflows that span architecture, RTL coding, verification, physical design, and post‑silicon validation. These processes are time‑consuming, costly, and prone to sign‑off delays, limiting a company's ability to meet market windows and achieve optimal power‑performance‑area (PPA) targets.
Solution
XORVIS AI delivers an AI‑native platform that embeds predictive intelligence across the entire silicon design flow—from architectural exploration through post‑silicon validation. The system continuously analyzes design intent, constraints, and historical data to generate optimized RTL, automate verification test creation, and recommend physical‑design tweaks that improve timing and routing efficiency. By surfacing risk indicators early and providing data‑driven sign‑off confidence, the platform shortens time‑to‑market, reduces net program spend, and helps designers hit higher PPA targets without additional manual effort. Collaboration features enable distributed design teams to work on a shared, cloud‑hosted workspace with versioned AI insights.
Target Audience
The primary customers are fabless semiconductor companies, ASIC design houses, and in‑house chip design teams that require end‑to‑end automation and performance optimization across the RTL‑to‑post‑silicon workflow.
Features
- AI‑driven design space exploration that proposes architecture and micro‑architecture alternatives based on performance, power, and area constraints.
- Automated RTL synthesis and code generation using large‑scale language models trained on industry‑standard HDL corpora.
- Machine‑learning‑augmented verification that auto‑creates directed test vectors, coverage metrics, and regression suites for both functional and formal checks.
- Predictive physical‑design optimization that adjusts placement, routing, and clock‑tree synthesis parameters to meet timing closure with higher yield.
- Post‑silicon validation analytics that correlate silicon measurements with pre‑silicon models to identify drift and suggest corrective actions.
- Seamless integration via RESTful APIs and plug‑ins for leading EDA tools (Cadence, Synopsys, Mentor) to embed AI recommendations directly into existing toolchains.
- Cloud‑based collaborative workspace with role‑based access, version control, and audit trails for secure multi‑team design iteration.
- Continuous learning pipeline that ingests new design data to refine models, ensuring improvements across successive projects.