用生成式Transformer自动设计高性能前缀加法器。
PrefixGPT: Prefix Adder Optimization by a Generative Pre-trained Transformer
- 将加法器拓扑转为坐标序列，生成时用合法性掩码保证设计有效。
- 找到新最优设计，面积延迟积提升7.7%，平均优化达79.1%。
- 适合硬件设计自动化与AI驱动电路优化的研究者。
前缀加法器广泛应用于计算密集型场景，因其速度快而备受青睐。然而，严格的布局规则与指数级增长的设计空间使其优化极具挑战。本文提出PrefixGPT，一种基于生成式预训练变压器（GPT）的模型，可从零开始直接生成优化的前缀加法器。该方法将加法器拓扑表示为二维坐标序列，并在生成过程中使用合法性掩码，确保每个设计均合法。PrefixGPT采用定制化的仅解码器架构，先在随机合成的有效前缀加法器语料上进行预训练以学习设计规则，再通过微调在设计空间中探索更优解。相比现有方法，PrefixGPT不仅发现了一种新的最优设计，使面积延迟积（ADP）提升7.7%，且整体探索质量显著提升，平均ADP降低达79.1%。这证明了类似GPT的模型在掌握复杂硬件设计原理后，可高效实现设计优化。
原文摘要 · Abstract (English)
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.
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