Approximate Computing: An Energy-Efficient Computing Technique for Error Resilient Applications

Abstract

Approximate computing has become a well-known computing technique in recent years. It relies on the ability of many systems and applications to self-heal or to tolerate some loss of quality or optimality in the computed result. The main idea is to exploit the inherent error resiliency or error tolerance of the system to achieve energy efficiency, or in other words, trading accuracy with energy consumption. Such trade-off, in most cases, is also associated with performance improvements like faster operations, area reduction etc. Fortunately, most of the heavy workloads nowadays exhibit intrinsic application resilience. In most multimedia applications, the final output is interpreted by human senses, which are not perfect. This fact averts the need to produce exact outputs. Recent research efforts have quantitatively ascertained the high degree of inherent resilience in many applications. For example, our analysis of a benchmark suite of 12 recognition, vision and multimedia applications shows that on average, 83% of the runtime is spent in computations that can tolerate at least some degree of approximation. Therefore, there is a potential to leverage inherent resilience in a broad context. Let us consider digital signal processing (DSP). In multi-media applications, DSP blocks implement image, sound and video processing algorithms, where the final output is either an image or sound or a video for human senses. When interpreting an image or a sound or a video, human beings have limited perceptual capacities. This allows the system to be flexible in producing quality outputs. As an example, the relaxation on numerical precision provides some freedom to carry out imprecise or approximate computation. Another example of approximate signal processing is the utilization of incremental refinement. It is evident that the tradeoffs may allow approximate computing to handle tasks beyond what we can do with traditional computing.There are different levels of design abstraction where approximate computing methods can be implemented. In this paper we will briefly describe different approaches that we have developed to implement approximate hardware for error resilient applications.