Abstract A Least-Mean-Square (LMS) based Adaptive Decision Feedback Equalizer (ADFE) structure using Distributed Arithmetic (DA) is presented. The filtering and weight-updating operations of the Feed Forward Filter (FFF) and Feedback Filter (FBF) of the ADFE have been recast using DA framework in order to obtain efficient multiplierless realization. Unlike, the DA based realization of the fixed coefficient filter, in case of adaptive filters, the updating of partial-products from time to time is a difficult task. We proposed an efficient technique which uses an auxiliary memory to perform the weight-update operation. This memory is updated from time to time using a register which stores the newest sample of the filter input. The proposed architecture is hardware efficient in the sense that it uses no multiplier units and fewer adders compared to existing architecture. For instance, for an ADFE with FBF length is equal to 8, with proper choice of parameters in the DA structure, the proposed architecture uses around 20% less chip area and power compared to most recent architecture existing in the literature. Simulation results show that the convergence characteristics of the proposed DA based LMS ADFE is almost similar to conventional multiply-accumulate (MAC) based realization.