Reducing power consumption in AI accelerators is increasingly important. Approximate computing can reduce power consumption while keeping the accuracy loss small. Since multipliers are power-hungry components in AI models, this paper focuses on synthesizing low-power approximate multipliers (AxMs). Unlike prior works that design AxMs separately from AI model training, we present TRAM, which jointly optimizes the AxM structure and AI model parameters to lower power with small accuracy loss. Experiments show that compared to state-of-the-art AxMs, TRAM achieves up to 25.05% AxM power reduction on CNNs with CIFAR-10, and reduces power by up to 27.09% on vision transformers with ImageNet.

翻译：降低AI加速器中的功耗日益重要。近似计算可在保持精度损失较小的同时降低功耗。由于乘法器是AI模型中高功耗的组件，本文聚焦于合成低功耗近似乘法器（AxMs）。与先前将AxM设计与AI模型训练分离的研究不同，我们提出TRAM，该方法联合优化AxM结构与AI模型参数，以在精度损失较小的前提下降低功耗。实验表明，相较于最先进的AxMs，TRAM在基于CIFAR-10的CNN上实现了最高25.05%的AxM功耗降低，并在基于ImageNet的视觉Transformer上实现了最高27.09%的功耗降低。