GPTAC: Domain-Specific Generative Pre-Trained Model for Approximate Circuit Design Exploration

Automatically designing fast and low-cost digital circuits is challenging because of the discrete nature of circuits and the enormous design space, particularly in the exploration of approximate circuits. However, recent advances in generative artificial intelligence (GAI) have shed light to address these challenges. In this work, we present GPTAC, a domain-specific generative pre-trained (GPT) model customized for designing approximate circuits. By specifying the desired circuit accuracy or area, GPTAC can automatically generate an approximate circuit using its generative capabilities. We represent circuits using domain-specific language tokens, refined through a hardware description language keyword filter applied to gate-level code. This representation enables GPTAC to effectively learn approximate circuits from existing datasets by leveraging the GPT language model, as the training data can be directly derived from gate-level code. Additionally, by focusing on a domain-specific language, only a limited set of keywords is maintained, facilitating faster model convergence. To improve the success rate of the generated circuits, we introduce a circuit check rule that masks the GPTAC inference results when necessary. The experiment indicated that GPTAC is capable of producing approximate multipliers in under 15 seconds while utilizing merely 4GB of GPU memory, achieving a 10-40% reduction in area relative to the accuracy multiplier depending on various accuracy needs.