Analysis and Design of Approximate Inner-Product Architectures Based on Distributed Arithmetic

Distributed arithmetic (DA) based architectures are popularly used for inner-product computation in various applications. Existing literature shows that the use of approximate DA-architectures in error resilient applications provides a significant improvement in the overall efficiency of the system. Based on precise error analysis, we find that the existing methods introduce large truncation error in the computation of the final inner-product. Therefore, to have a suitable trade-off between the overall hardware complexity and truncation error, a weight-dependent truncation approach is proposed in this paper. The overall efficiency of the structure is further enhanced by incorporating an input truncation strategy in the proposed method. It is observed that the area, time and energy efficiency of the proposed designs are superior to the existing designs with significantly lower truncation error. Evaluation in the case of noisy image smoothing application is also shown in this paper.