标题： GENIAL：通过网络反演进行生成设计空间探索以实现低功耗算法逻辑单元 显示英文标题

标题： GENIAL: Generative Design Space Exploration via Network Inversion for Low Power Algorithmic Logic Units

摘要： 随着AI工作负载的增加，优化算术单元对于减少数字系统的占地面积变得越来越重要。传统的设计流程通常依赖于手动或基于启发式的优化，它们在彻底探索广阔的设计空间方面存在局限性。在本文中，我们介绍了GENIAL，一个基于机器学习的框架，用于算术单元（特别是乘法器）的自动生成和优化。GENIAL的核心是一个基于Transformer的代理模型，该模型经过两个阶段的训练，包括自监督预训练和监督微调，以从抽象的设计表示中稳健地预测关键硬件指标，如功耗和面积。通过反转代理模型，GENIAL能够高效地搜索新的操作数编码，从而直接最小化特定输入数据分布下算术单元的功耗。在大型数据集上的大量实验表明，GENIAL在样本效率方面始终优于其他方法，并且更快地收敛到优化设计。这使得能够在循环中部署高努力逻辑综合优化流程，提高代理模型的准确性。值得注意的是，与传统的补码相比，GENIAL自动发现的编码在代表性AI工作负载中的乘法器内实现了高达18%的切换活动节省。我们还通过在有限状态机上实现显著改进，展示了我们方法的通用性，突显了GENIAL在广泛逻辑函数中的适用性。这些进展共同标志着在数字系统中自动化结果质量优化的组合电路生成方面迈出了重要的一步。 显示英文摘要

摘要： As AI workloads proliferate, optimizing arithmetic units is becoming increasingly important to reduce the footprint of digital systems. Conventional design flows, which often rely on manual or heuristics-based optimization, are limited in their ability to thoroughly explore the vast design space. In this paper, we introduce GENIAL, a machine learning-based framework for the automatic generation and optimization of arithmetic units, more specifically multipliers. At the core of GENIAL is a Transformer-based surrogate model trained in two stages, involving self-supervised pretraining followed by supervised finetuning, to robustly forecast key hardware metrics such as power and area from abstracted design representations. By inverting the surrogate model, GENIAL efficiently searches for new operand encodings that directly minimize power consumption in arithmetic units for specific input data distributions. Extensive experiments on large datasets demonstrate that GENIAL is consistently more sample efficient than other methods, and converges faster towards optimized designs. This enables to deploy a high-effort logic synthesis optimization flow in the loop, improving the accuracy of the surrogate model. Notably, GENIAL automatically discovers encodings that achieve up to 18% switching activity savings within multipliers on representative AI workloads compared with the conventional two's complement. We also demonstrate the versatility of our approach by achieving significant improvements on Finite State Machines, highlighting GENIAL's applicability for a wide spectrum of logic functions. Together, these advances mark a significant step toward automated Quality-of-Results-optimized combinational circuit generation for digital systems.