Designing Energy-Efficient Arithmetic Operators Using Inexact Computing

It is widely acknowledged that the exponentially improving benefits of sustained technology scaling prophesied by the Moore's Law would end within the next decade or so, attributed primarily to an understanding that switching devices can no longer function deterministically as feature sizes are scaled down to the molecular levels. The benefits of Moore's Law could, however, continue provided systems with probabilistic or “error-prone” elements could still process information usefully. We believe that this is indeed possible in contexts where the “quality” of the results of the computation is perceptually determined by our senses—audio and video information being significant examples. To demonstrate this principle, we will show how such “inexact” computing based devices, circuits and computing architectures can be used effectively to realize many ubiquitous energy-constrained error-resilient applications. Further, we show that significant energy, performance and area gains can be achieved, while trading a perceptually tolerable level of error–that will be ultimately determined based on neurobiological models—applied in the context of video and audio data in digital signal processing. This design philosophy of inexact computing is of particular interest in the domain of embedded and (portable) multimedia applications and in application domains of budding interest such as recognition and data mining, all of which can tolerate inaccuracies to varying extents or can synthesize accurate (or sufficient) information even from inaccurate computations!