# TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators Source: [TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators]() Collection index: [llms.txt]() Data updated: 2026-05-13T05:00:05.563Z Authors: Chang Meng, Hanyu Wang, Yuyang Ye, Mingfei Yu, Wayne Burleson, Giovanni De Micheli Published: 2026-05-06T20:39:32.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CIFAR-10 (Accuracy, Norm. Energy, Runtime)](): Image Classification; CIFAR-10; Accuracy: 93.71. - [Image Classification on ImageNet (val) (Accuracy and Norm. AxM Power)](): Image Classification; ImageNet (val); Accuracy: 79.54. ## Original sources and implementation [Original paper]() [PDF]()