Approximate computing for energy-efficient error-resilient multimedia systems

The rapid advancement in scaled silicon technology has resulted in the influx of numerous consumer devices with a plethora of applications. Multimedia applications which use image and video processing, pattern or facial recognition, data mining and synthesis have seen a significant increase in user base. These applications not only demand complex signal processing of digital data to achieve quality requirements specified by the user, but also need to operate in an energy-efficient manner, posing a significant design challenge. It should also be noted that majority of these applications have an inherent error-resiliency. This arises from the fact that: (a) these algorithms have to be noise tolerant to deal with real world input data, (b) large data sets are processed frequently with significant redundancy, (c) statistical or probabilistic computations are used in several cases, (d) human perception does not discern a small amount of error in output.