PrefixGPT: Prefix Adder Optimization by a Generative Pre-trained Transformer
This addresses the problem of efficient hardware design optimization for compute-intensive applications, representing an incremental advancement by applying GPT-style models to a domain-specific task.
The paper tackled the challenge of designing optimized prefix adders by introducing PrefixGPT, a generative pre-trained Transformer that generates valid prefix adders from scratch, resulting in a new optimal design with a 7.7% improved area-delay product and up to 79.1% lower average ADP.
Prefix adders are widely used in compute-intensive applications for their high speed. However, designing optimized prefix adders is challenging due to strict design rules and an exponentially large design space. We introduce PrefixGPT, a generative pre-trained Transformer (GPT) that directly generates optimized prefix adders from scratch. Our approach represents an adder's topology as a two-dimensional coordinate sequence and applies a legality mask during generation, ensuring every design is valid by construction. PrefixGPT features a customized decoder-only Transformer architecture. The model is first pre-trained on a corpus of randomly synthesized valid prefix adders to learn design rules and then fine-tuned to navigate the design space for optimized design quality. Compared with existing works, PrefixGPT not only finds a new optimal design with a 7.7% improved area-delay product (ADP) but exhibits superior exploration quality, lowering the average ADP by up to 79.1%. This demonstrates the potential of GPT-style models to first master complex hardware design principles and then apply them for more efficient design optimization.