Improving the quality degradation of dynamically configurable approximate multipliers via data correlation

In the last few years, dynamically configurable approximate multipliers have been explored to tune the energy-quality trade-off in error-tolerant applications at runtime. Typically, the multiplier accuracy is adjusted by adding a constant correction factor equal to the multiplier mean error to the result, which is found offline assuming a predetermined input distribution. This paper describes a simple approach to update the correction term at runtime, thus adapting it to the actual incoming inputs. It takes advantage of the spatial and/or temporal correlation typically shown by input data in error-tolerant applications, such as image and video processing. When applied to a typical case study implemented with a commercial UTBB FDSOI 28 nm technology, the proposed approach shows an energy reduction of up to 34% at iso-quality and a quality improvement of up to +9 dB, −4× and +35% at iso-energy, in terms of peak-to-noise ratio (PSNR), normalized error distance (NED) and structural similarity index metric (SSIM) respectively, compared to the traditional technique based on a constant correction factor.