Tuning the Computational Effort: An Adaptive Accuracy-aware Approach Across System Layers

This thesis introduces a novel methodology to realize accuracy-aware systems, which will help designers integrate accuracy awareness into their systems. It proposes an adaptive accuracy-aware approach across system layers that addresses current challenges in that domain, combining and tuning accuracy-aware methods on different system layers. To widen the scope of accuracy-aware computing including approximate computing for other domains, this thesis presents innovative accuracy-aware methods and techniques for different system layers. The required tuning of the accuracy-aware methods is integrated into a configuration layer that tunes the available knobs of the accuracy-aware methods integrated into a system

Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications

The challenging deployment of compute-intensive applications from domains such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to improve the energy efficiency and/or performance. This radical paradigm shift has attracted interest from both academia and industry, resulting in significant research on approximation techniques and methodologies at different design layers (from system down to integrated circuits). Motivated by the wide appeal of Approximate Computing over the last 10 years, we conduct a two-part survey to cover key aspects (e.g., terminology and applications) and review the state-of-the art approximation techniques from all layers of the traditional computing stack. In Part II of our survey, we classify and present the technical details of application-specific and architectural approximation techniques, which both target the design of resource-efficient processors/accelerators & systems. Moreover, we present a detailed analysis of the application spectrum of Approximate Computing and discuss open challenges and future directions.Comment: Under Review at ACM Computing Survey

Approximation Opportunities in Edge Computing Hardware : A Systematic Literature Review

With the increasing popularity of the Internet of Things and massive Machine Type Communication technologies, the number of connected devices is rising. However, while enabling valuable effects to our lives, bandwidth and latency constraints challenge Cloud processing of their associated data amounts. A promising solution to these challenges is the combination of Edge and approximate computing techniques that allows for data processing nearer to the user. This paper aims to survey the potential benefits of these paradigms’ intersection. We provide a state-of-the-art review of circuit-level and architecture-level hardware techniques and popular applications. We also outline essential future research directions.publishedVersionPeer reviewe

Infrastructures and Compilation Strategies for the Performance of Computing Systems

This document presents our main contributions to the field of compilation, and more generally to the quest of performance ofcomputing systems.It is structured by type of execution environment, from static compilation (execution of native code), to JIT compilation, and purelydynamic optimization. We also consider interpreters. In each chapter, we give a focus on the most relevant contributions.Chapter 2 describes our work about static compilation. It covers a long time frame (from PhD work 1995--1998 to recent work on real-timesystems and worst-case execution times at Inria in 2015) and various positions, both in academia and in the industry.My research on JIT compilers started in the mid-2000s at STMicroelectronics, and is still ongoing. Chapter 3 covers the results we obtained on various aspects of JIT compilers: split-compilation, interaction with real-time systems, and obfuscation.Chapter 4 reports on dynamic binary optimization, a research effort started more recently, in 2012. This considers the optimization of a native binary (without source code), while it runs. It incurs significant challenges but also opportunities.Interpreters represent an alternative way to execute code. Instead of native code generation, an interpreter executes an infinite loop thatcontinuously reads a instruction, decodes it and executes its semantics. Interpreters are much easier to develop than compilers,they are also much more portable, often requiring a simple recompilation. The price to pay is the reduced performance. Chapter 5presents some of our work related to interpreters.All this research often required significant software infrastructures for validation, from early prototypes to robust quasi products, andfrom open-source to proprietary. We detail them in Chapter 6.The last chapter concludes and gives some perspectives