Empowering the next generation of Hardware-design AI models, by closing the gap of Verilog data scarcity, inconsistent quality, and restrictive licensing.
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Product
The ARTL agentic AI platform models chip architectures in Python and seamlessly converts them into Verilog using our proprietary Python-to-Verilog compiler. This pipeline generates novel, fully verified, and copyright-free Verilog datasets at scale to train the next generation of AI models.
Microarchitecture Exploration
Evaluate Power, Performance, and Area (PPA) tradeoffs directly on the Python model prior to generating RTL.
Automated Verification
Automatically generate testbenches for each design to ensure rigorous verification against the original Python model.
Architectural Diversity
Generate multiple architectural variations for every design—each independently tested, synthesized, and analyzed.
Human-Like Code Styling
Leverage diverse syntax generation to produce rich, varied coding styles, avoiding predictable machine-generated patterns.
Targeted Bug Injection
Train models for root-cause analysis using sophisticated bug injection techniques targeting all stages of the design cycle.
Timing Closure Training
Utilize auto-pipelining technology to create designs with varying pipeline depths, simulating realistic timing violations.
Golden & Buggy Datasets
Deliver fully annotated datasets encompassing both correct-by-construction designs and explicitly flawed variations.
Dynamic Generation
Generate new designs, complex edge cases, and specific logic bugs entirely on demand.
Seamless Integration
Integrate seamlessly with industry-standard EDA workflows and open-source hardware design flows.
Proven in Complex Domains
Showcased across a multitude of complex designs, including ML accelerators, modems, SmartNICs, and cryptography.
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Reach out today to see how ARTL can power your hardware-design AI models.