{"ID":2838202,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19472","arxiv_id":"2511.19472","title":"PrefixGPT: Prefix Adder Optimization by a Generative Pre-trained Transformer","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.19472","url_pdf":"https://arxiv.org/pdf/2511.19472v2","authors":"[\"Ruogu Ding\",\"Xin Ning\",\"Ulf Schlichtmann\",\"Weikang Qian\"]","published":"2025-11-22T06:43:43Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.AR\"]","methods":"[\"Transformer\",\"LoRA\"]","has_code":false}