Energy Efficient Approximate Architecture for Error Tolerant Applications

Abstract

The energy-efficient circuits are one of the primary/major problems in many different areas of digital design, including embedded and battery-driven devices as well as data centers. During the last several years, wide variety of strategies have been developed with the goal of reducing the amounts of power and increasing the speed. One of these developing techniques that aims to make error-tolerant/resistant applications is called approximate computing, and it offers some exciting potential advantages. The traditional/conventional power vs performance trade-offs are expanded to include a third orthogonal dimension, that is introduced by approximate computing: design correctness. The fundamental idea behind such an approach is that if one relaxes the accuracy requirement on the output, they can realize significant savings in the hardware design metrics such as power consumption, design area, and critical path delay. This is the underlying principle that underpins such an approach. This paradigm, on the other hand, can only be used in contexts where there is already an established tolerance for relatively inconsequential and minor mistakes. Several examples of potential applications include those in the fields of media processing, machine learning, and data mining. It is interesting to note that in recent years, there has been a rise in the number of applications that rely heavily on data and machine learning, yet the importance of approximate computing has only increased/grown. newlineComputing based on approximations is becoming more popular as a computing paradigm for applications in computer vision, data analytics, and image/signal processing. The use of approximate computing is becoming more important/crucial in the current age of real-time applications. Adders are the primary component for the design and implementation of digital circuitry and signal processing applications in many different types of computers, including Digital Signal Processors (DSP) and microprocessors. The propagation delay in the carry chain is