AI RESEARCH
AxMoE: Characterizing the Impact of Approximate Multipliers on Mixture-of-Experts DNN Architectures
arXiv CS.LG
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ArXi:2605.04754v1 Announce Type: new Deep neural network (DNN) inference at the edge demands simultaneous improvements in accuracy, computational efficiency, and energy consumption. Approximate computing and Mixture-of-Experts (MoE) architectures have each been studied as independent routes towards efficient inference, the former by replacing exact arithmetic with low-power approximate multipliers, the latter by routing inputs through specialized expert sub-networks to enable conditional computation. However, their interaction remains entirely unexplored.