As Deep Neural Networks (DNNs) continue to drive advancements in artificial intelligence, the design of hardware accelerators faces growing concerns over embodied carbon footprint due to complex fabrication processes. 3D integration improves performance but introduces sustainability challenges, making carbon-aware optimization essential. In this work, we propose a carbon-efficient design methodology for 3D DNN accelerators, leveraging approximate computing and genetic algorithm-based design space exploration to optimize Carbon Delay Product (CDP). By integrating area-efficient approximate multipliers into Multiply-Accumulate (MAC) units, our approach effectively reduces silicon area and fabrication overhead while maintaining high computational accuracy. Experimental evaluations across three technology nodes (45nm, 14nm, and 7nm) show that our method reduces embodied carbon by up to 30% with negligible accuracy drop.

本研究针对深度神经网络(DNN)加速器在复杂制造过程中对碳足迹日益关注的问题，提出了一种碳高效设计方法。通过结合近似计算和基于遗传算法的设计空间探索，该方法优化了碳延迟产品(CDP)，在三个不同技术节点（45nm、14nm和7nm）上实现了最高30%的碳减排且几乎没有准确性下降。

碳高效3D深度神经网络加速：优化性能和可持续性