A Survey of Techniques for Approximate Computing

Abstract

Approximate computing trades off computation quality with effort expended, and as rising performance demands confront plateauing resource budgets, approximate computing has become not merely attractive, but even imperative. In this article, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units (e.g., CPU, GPU, and FPGA), processor components, memory technologies, and so forth, as well as programming frameworks for AC. We classify these techniques based on several key characteristics to emphasize their similarities and differences. The aim of this article is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.

Big Data in Organizations and the Role of Human Resource Management

“…There are two developments in computing that may intensify the impreciseness of big data further. On the one hand, there is approximate computing (Mittal 2016), in which the computer will tolerate some loss in correctness in order to become quicker or more energy efficient (Han & Orshansky 2013). Big data will not be exact.…”

Approximate Circuits in Low-Power Image and Video Processing: The Approximate Median Filter

“…In approximate computing, the requirement of exact equivalence between the specification and all implementations levels is relaxed in order to reduce power consumption or improve other system parameters such as performance [1], [7].…”

“…Mittal [1] discusses a wide spectrum of approximation techniques which include precision scaling, loop perforation, load value approximation, memorization, task dropping/skipping, memory access skipping, using different SW/HW versions, refresh rate reducing in memory, inexact read/write and relaxed synchronization.…”

“…Approximate computing [1] exploits the fact that many applications (image and video processing in particular) are highly error resilient. If occasional errors are acceptable by the users -which is possible because the users as consumers of the outputs of these applications are often unable to recognize small imperfections in images or video sequences -implementations of these applications can be simplified.…”

Edge-Supported Approximate Analysis for Long Running Computations

“…Thus, approximate computing provides a balance between the level of accuracy required by the user and that provided by the computing system, to achieve a spectrum of optimizations. Such techniques are applicable to a wide range of applications/frameworks, for example, data analytics, scientific computing, multimedia and signal processing, machine learning and MapReduce [6,12].…”