Query: approximate computing

Candidate papers:

abstract: Computing systems are conventionally designed to operate as accurately as possible. However, this trend faces severe technology challenges, such as power consumption, circuit reliability, and high performance. For nearly half a century, performance and power consumption of computing systems have been consistently improved by relying mostly on technology scaling. As per Dennard's scaling, the size of a transistor has been considerably shrunk and the supply voltage has been reduced over the years, such that circuits operate at higher frequencies but nearly at the same power dissipation level. However, as Dennard's scaling tends toward an end, it is difficult to further improve performance under the same power constraints. Power consumption has been a major concern, and it is now an industry wide problem of critical importance. In addition to power, reliability deteriorates when the feature size of complementary metal oxide semiconductor (CMOS) technology is reduced below 7 nm, because parameter variations and faults at advanced nanoscales become difficult to control and prevent. Thus, to ensure the complete accuracy of signals, logic values, devices, and interconnects, manufacturing and verification costs will increase significantly.
author_names: Weiqiang Liu

Fabrizio Lombardi

Michael Shulte
title: A Retrospective and Prospective View of Approximate Computing [Point of View}
venue: Proc. IEEE
year: 2020

abstract: Numerous application domains (e.g. signal and image processing, computer graphics, computer vision, and machine learning) are inherently error tolerant, which can be exploited to produce approximate ASIC implementations with low power consumption at the expense of negligible or small reductions in application quality. A major challenge is the need for approximate and high level design generation tools that can automatically work on arbitrary designs. In this article, we provide an expanded and improved treatment of our ABACUS methodology, which aims to automatically generate approximate designs directly from their behavioral register transfer level (RTL) descriptions, enabling a wider range of possible approximations. ABACUS starts by creating an abstract syntax tree (AST) from the input behavioral RTL description of a circuit, and then applies variant operators to the AST to create acceptable approximate designs. The devised variant operators include data type simplifications, arithmetic operation approximations, arithmetic expressions transformations, variable to constant substitutions, and loop transformations. A design space exploration technique is devised to explore the space of possible variant approximate designs and to identify the designs along the Pareto frontier that represents the trade off between accuracy and power consumption. In addition, ABACUS prioritizes generating approximate designs that, when synthesized, lead to circuits with simplified critical paths, which are exploited to realize complementary power savings through standard voltage scaling. We integrate ABACUS with a standard ASIC design flow, and evaluate it on four realistic benchmarks from three different domains machine learning, signal processing, and computer vision. Our tool automatically generates many approximate design variants with large power savings, while maintaining good accuracy. We demonstrate the scalability of ABACUS by parallelizing the flow and use of recent standard synthesis tools. Compared to our previous efforts, the new ABACUS tool provides up to 20.5x speed up in runtime, while able to generate approximate circuits that lead to additional power savings reaching up to 40 percent.
author_names: Kumud Nepal

Soheil Hashemi

Hokchhay Tann

R Iris Bahar

Sherief Reda
title: Automated High Level Generation of Low Power Approximate Computing Circuits
venue: IEEE Transactions on Emerging Topics in Computing
year: 2019

abstract: In scientific computing, the acceleration of atomistic computer simulations by means of custom hardware is finding ever growing application. A major limitation, however, is that the high efficiency in terms of performance and low power consumption entails the massive usage of low precision computing units. Here, based on the approximate computing paradigm, we present an algorithmic method to rigorously compensate for numerical inaccuracies due to low accuracy arithmetic operations, yet still obtaining exact expectation values using a properly modified Langevin type equation.
author_names: Varadarajan Rengaraj

Michael Lass

Christian Plessl

Thomas D Kuhne
title: Accurate Sampling with Noisy Forces from Approximate Computing
venue: Comput.
year: 2020

abstract: This work is a survey on approximate computing and its impact on fault tolerance, especially for safety critical applications. It presents a multitude of approximation methodologies, which are typically applied at software, architecture, and circuit level. Those methodologies are discussed and compared on all their possible levels of implementations (some techniques are applied at more than one level) Approximation is also presented as a means to provide fault tolerance and high reliability: Traditional error masking techniques, such as triple modular redundancy, can be approximated and thus have their implementation and execution time costs reduced compared to the state of the art.
author_names: Gennaro Severino Rodrigues

Fernanda Lima Kastensmidt

Alberto Bosio
title: Survey on Approximate Computing and Its Intrinsic Fault Tolerance
year: 2020

abstract: In this article, we review some of the classical methods used for quickly obtaining low precision approximations to the elementary functions. Then, for each of the three main classes of elementary function algorithms (shift and add algorithms, polynomial or rational approximations, and table based methods) and for the additional, specific to approximate computing, "bit manipulation" techniques, we examine what can be done for obtaining very fast estimates of a function, at the cost of a (controlled) loss in terms of accuracy.
author_names: Jean-Michel Muller
title: Elementary Functions and Approximate Computing
venue: Proceedings of the IEEE
year: 2020

abstract: The Internet of Things significantly increases the amount of data generated, straining the processing capability of current computing systems. Approximate computing is a promising solution to accelerate computation by trading off energy and accuracy. In this paper, we propose a resistive content addressable memory (CAM) accelerator, called RCA, which exploits data locality to have an approximate memory based computation. RCA stores high frequency patterns and performs computation inside CAM without using processing cores. During execution time, RCA searches an input operand among all prestored values on a CAM and returns the row with the nearest distance. To manage accuracy, we use a distance metric which considers the impact of each bit indices on computation accuracy. We evaluate an application of proposed RCA on CPU approximation, where RCA can be used as a stand alone or as a hybrid computing unit besides CPU cores for tunable CPU approximation. We evaluate the architecture of the proposed RCA using HSPICE and multi2sim by testing our results on x86 CPU processor. Our evaluation shows that RCA can accelerate CPU computation by 12.6x and improve the energy efficiency by 6.6x as compared to a traditional CPU architecture, while providing acceptable quality of service.
author_names: Mohsen Imani

Daniel Peroni

Abbas Rahimi

Tajana Simunic Rosing
title: Resistive CAM Acceleration for Tunable Approximate Computing
venue: IEEE Transactions on Emerging Topics in Computing
year: 2019

abstract: This paper presents a novel method for applying approximate computing at the level of a complete application. The method decomposes the application into processing blocks which types define the classes of approximate computing techniques they may tolerate. By applying these approximation techniques to the most computationally intensive blocks, drastic energy reduction can be obtained at a limited cost in terms of Quality of Service. The algorithmic level approximate computing method is applied to a software High Efficiency Video Coding (HEVC) video decoder. The method is shown to offer multiple trade offs between the quality of the decoded video and the energy required for the decoding process. The algorithmic level approximate computing method offers new possibilities in terms of application energy budgeting. Energy reductions of up to 40 percent are demonstrated for a limited degradation of the application Quality of Service.
author_names: Erwan Nogues

Daniel Menard

Maxime Pelcat
title: Algorithmic Level Approximate Computing Applied to Energy Efficient HEVC Decoding
venue: IEEE Transactions on Emerging Topics in Computing
year: 2019

abstract: Approximate computing systems improve energy efficiency and computation speed at the cost of reduced accuracy on system outputs. Existing efforts mainly explore the feasible approximation mechanisms and their implementation methods. There is limited work that investigates the security threats brought by approximate computing. To fill this gap, we first analyze the approximate mechanisms used in approximate system, software, storage, and arithmetic circuits, and then propose potential attacks that will challenge the integrity and security of approximate systems. Some illustrative examples are provided accordingly to showcase the consequences of the proposed new attacks.
author_names: Pruthvy Yellu

Novak Boskov

Michel A Kinsy

Qiaoyan Yu
title: Security Threats in Approximate Computing Systems
venue: ACM Great Lakes Symposium on VLSI
year: 2019

abstract: In the autonomic computing context, the system is perceived as a set of autonomous elements capable of self management, where end users define high level goals and the system shall adapt to achieve the desired behaviour. Runtime adaptation creates several optimization opportunities, especially if we consider approximate computing applications, where it is possible to trade off the accuracy of the result and the performance. Given that modern systems are limited by the power dissipated, autonomic computing is an appealing approach to increase the computation efficiency. In this paper, we introduce mARGOt, a dynamic autotuning framework to enhance the target application with an adaptation layer to provide self optimization capabilities. The framework is implemented as a C+ library that works at function level and provides to the application a mechanism to adapt in a reactive and a proactive way. Moreover, the application is capable to change dynamically its requirements and to learn online the underlying application knowledge. We evaluated the proposed framework in three real life scenarios, ranging from embedded to HPC applications. In the three use cases, experimental results demonstrate how, thanks to mARGOt, it is possible to increase the computation efficiency by adapting the application at runtime with a limited overhead.
author_names: Davide Gadioli

Emanuele Vitali

Gianluca Palermo

Cristina Silvano
title: mARGOt: A Dynamic Autotuning Framework for Self Aware Approximate Computing
venue: IEEE Transactions on Computers
year: 2019

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.
author_names: Sparsh Mittal
title: A Survey of Techniques for Approximate Computing
venue: ACM Comput. Surv.
year: 2016