arXiv:2605.31040v1 Announce Type: new Abstract: Developing effective representations for register transfer level (RTL) designs is crucial for accelerating the hardware design workflow. Existing approaches, however, typically rely on a single data modality, either the RTL code or its associated graph-based representation, limiting the expressiveness and generalization ability of the learned representations. For RTL, the control data flow graph (CDFG) offers a comprehensive structural representation that preserves complete information, while the code modality explicitly encodes semantic and func
The increasing complexity of hardware design and the growing need for efficient hardware acceleration in AI and other fields are driving continuous innovation in RTL representation learning.
Improved RTL representation learning can significantly accelerate hardware design workflows, leading to faster innovation cycles and more efficient silicon for various applications, directly impacting manufacturing and AI capabilities.
By unifying code and graph representations, UniRTL offers more robust and generalized representations for register transfer level designs, potentially leading to more automated and optimized chip design processes.
- · Semiconductor manufacturers
- · AI hardware developers
- · EDA software companies
- · High-performance computing sector
- · Traditional manual hardware design methodologies
- · Companies reliant on less efficient RTL analysis tools
Faster and more accurate hardware design and verification becomes possible.
Reduced time-to-market for advanced chips and specialized AI accelerators, impacting global technology leadership.
Democratization of complex hardware design, enabling smaller teams to develop sophisticated silicon, potentially decentralizing parts of the compute supply chain.
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Read at arXiv cs.LG