Smarter RTL Code Optimization
Combining LLMs with Symbolic Reasoning for Superior Circuit Design
This research introduces a novel approach that combines Large Language Models (LLMs) with neuron-inspired symbolic reasoning to optimize Register Transfer Level (RTL) code, achieving superior performance over manual rewriting and traditional compiler-based approaches.
- Improves power, performance, and area (PPA) metrics for digital circuits
- Uses an innovative hybrid approach that leverages both LLM capabilities and symbolic reasoning
- Addresses the limitations of both manual RTL optimization (time-consuming, error-prone) and compiler-based approaches (difficulty with complex constraints)
- Provides a more efficient pathway for hardware engineers to optimize circuit designs
This advancement matters for engineering teams by reducing development time while improving circuit quality, potentially accelerating hardware development cycles and enabling more efficient electronic devices.