Input-aware accuracy characterization for approximate circuits

It has been a while since Approximate Computing (AxC) is applied systematically at various abstraction levels to increase the efficiency of several applications such as image processing and machine learning. Despite its benefit, AxC is still agnostic concerning the specific workload (i.e., input data to be processed) of a given application. For instance, in signal processing applications (such as a filter), some inputs are constants (filter coefficients). Meaning that a further level of approximation can be introduced by considering the specific input distribution. This approach has been referred to as "input-aware approximation". In this paper, we explore how the input-aware approximate design approach can become part of a systematic, generic, and automatic design flow by knowing the data distribution. In particular, we show how input distribution can affect the error characteristics of an approximate arithmetic circuit and also the advantage of considering the data distribution by designing an input-aware approximate multiplier specifically intended for a high-pass FIR filter, where the coefficients are constant. Experimental results show that we can significantly reduce power consumption while keeping an error rate lower than state-of-the-art approximate multipliers.