A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems

To exploit the power of modern heterogeneous multiprocessor embedded platforms on partitioned applications, the designer usually needs to efficiently map and schedule all the tasks and the communications of the application, respecting the constraints imposed by the target architecture. Since the problem is heavily constrained, common methods used to explore such design space usually fail, obtaining low-quality solutions. In this paper, we propose an ant colony optimization (ACO) heuristic that, given a model of the target architecture and the application, efficiently executes both scheduling and mapping to optimize the application performance. We compare our approach with several other heuristics, including simulated annealing, tabu search, and genetic algorithms, on the performance to reach the optimum value and on the potential to explore the design space. We show that our approach obtains better results than other heuristics by at least 16% on average, despite an overhead in execution time. Finally, we validate the approach by scheduling and mapping a JPEG encoder on a realistic target architecture

Hardware-software codesign in a high-level synthesis environment

Interfacing hardware-oriented high-level synthesis to software development is a computationally hard problem for which no general solution exists. Under special conditions, the hardware-software codesign (system-level synthesis) problem may be analyzed with traditional tools and efficient heuristics. This dissertation introduces a new alternative to the currently used heuristic methods. The new approach combines the results of top-down hardware development with existing basic hardware units (bottom-up libraries) and compiler generation tools. The optimization goal is to maximize operating frequency or minimize cost with reasonable tradeoffs in other properties. The dissertation research provides a unified approach to hardware-software codesign. The improvements over previously existing design methodologies are presented in the frame-work of an academic CAD environment (PIPE). This CAD environment implements a sufficient subset of functions of commercial microelectronics CAD packages. The results may be generalized for other general-purpose algorithms or environments. Reference benchmarks are used to validate the new approach. Most of the well-known benchmarks are based on discrete-time numerical simulations, digital filtering applications, and cryptography (an emerging field in benchmarking). As there is a need for high-performance applications, an additional requirement for this dissertation is to investigate pipelined hardware-software systems\u27 performance and design methods. The results demonstrate that the quality of existing heuristics does not change in the enhanced, hardware-software environment

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201

Generic Connectivity-Based CGRA Mapping via Integer Linear Programming

Coarse-grained reconfigurable architectures (CGRAs) are programmable logic devices with large coarse-grained ALU-like logic blocks, and multi-bit datapath-style routing. CGRAs often have relatively restricted data routing networks, so they attract CAD mapping tools that use exact methods, such as Integer Linear Programming (ILP). However, tools that target general architectures must use large constraint systems to fully describe an architecture's flexibility, resulting in lengthy run-times. In this paper, we propose to derive connectivity information from an otherwise generic device model, and use this to create simpler ILPs, which we combine in an iterative schedule and retain most of the exactness of a fully-generic ILP approach. This new approach has a speed-up geometric mean of 5.88x when considering benchmarks that do not hit a time-limit of 7.5 hours on the fully-generic ILP, and 37.6x otherwise. This was measured using the set of benchmarks used to originally evaluate the fully-generic approach and several more benchmarks representing computation tasks, over three different CGRA architectures. All run-times of the new approach are less than 20 minutes, with 90th percentile time of 410 seconds. The proposed mapping techniques are integrated into, and evaluated using the open-source CGRA-ME architecture modelling and exploration framework.Comment: 8 pages of content; 8 figures; 3 tables; to appear in FCCM 2019; Uses the CGRA-ME framework at http://cgra-me.ece.utoronto.ca

MULTI-OBJECTIVE DESIGN AUTOMATION FOR RECONFIGURABLE MULTI-PROCESSOR SYSTEMS

Ph.DDOCTOR OF PHILOSOPH

Software development of reconfigurable real-time systems : from specification to implementation

This thesis deals with reconfigurable real-time systems solving real-time tasks scheduling problems in a mono-core and multi-core architectures. The main focus in this thesis is on providing guidelines, methods, and tools for the synthesis of feasible reconfigurable real-time systems in a mono-processor and multi-processor architectures. The development of these systems faces various challenges particularly in terms of stability, energy consumption, response and blocking time. To address this problem, we propose in this work a new strategy of i) placement and scheduling of tasks to execute real-time applications on mono-core and multi-core architectures, ii) optimization step based on Mixed integer linear programming (MILP), and iii) guidance tool that assists designers to implement a feasible multi-core reconfigurable real-time from specification level to implementation level. We apply and simulate the contribution to a case study, and compare the proposed results with related works in order to show the originality of this methodology.Echtzeitsysteme laufen unter harten Bedingungen an ihre Ausführungszeit. Die Einhaltung der Echtzeit-Bedingungen bestimmt die Zuverlässigkeit und Genauigkeit dieser Systeme. Neben den Echtzeit-Bedingungen müssen rekonfigurierbare Echtzeitsysteme zusätzliche Rekonfigurations-Bedingungen erfüllen. Diese Arbeit beschäftigt sich mit rekonfigurierbaren Echtzeitsystemen in Mono- und Multicore-Architekturen. An die Entwicklung dieser Systeme sind verschiedene Anforderungen gestellt. Insbesondere muss die Rekonfigurierbarkeit beachtet werden. Dabei sind aber Echtzeit-Bedingungen und Ressourcenbeschränkungen weiterhin zu beachten. Darüber hinaus werden die Kosten für die Entwicklung dieser Systeme insbesondere durch falsche Designentscheidungen in den frühen Phasen der Entwicklung stark beeinträchtigt. Das Hauptziel in dieser Arbeit liegt deshalb auf der Bereitstellung von Handlungsempfehlungen, Methoden und Werkzeugen für die zielgerichtete Entwicklung von realisierbaren rekonfigurierbaren Echtzeitsystemen in Mono- und Multicore-Architekturen. Um diese Herausforderungen zu adressieren wird eine neue Strategie vorgeschlagen, die 1) die Funktionsallokation, 2) die Platzierung und das Scheduling von Tasks, 3) einen Optimierungsschritt auf der Basis von Mixed Integer Linear Programming (MILP) und 4) eine entscheidungsunterstützende Lösung umfasst, die den Designern hilft, eine realisierbare rekonfigurierbare Echtzeitlösung von der Spezifikationsebene bis zur Implementierungsebene zu entwickeln. Die vorgeschlagene Methodik wird auf eine Fallstudie angewendet und mit verwandten Arbeiten vergliche