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The current embedded processors often do not satisfy increasingly demanding computation requirements of embedded applications within acceptable energy efficiency, whereas application-specific integrated circuits require excessive design costs. In the Stanford Elm project, it was identified that instruction and data delivery, not computation, dominate the energy consumption of embedded processors. Consequently, the energy efficiency of delivering instructions and data must be sufficiently improved to close the efficiency gap between application-specific integrated circuits and programmable embedded processors. This dissertation demonstrates that the compiler and run-time system can play a crucial role in improving the energy efficiency of delivering instructions and data. Regarding instruction delivery, I present a compiler algorithm that manages L0 instruction scratch-pad memories that reside between processor cores and L1 caches. Despite the lack of tags, the scratch-pad memories with our algorithm can achieve lower miss rates than caches with the same capacities, saving significant instruction delivery energy. Regarding data delivery, I present methods that minimize memory-space requirements for parallelizing stream applications, applications that are commonly found in the embedded domain. When stream applications are parallelized in pipelining, large enough buffers are required between pipeline stages to sustain the throughput (e.g., double buffering). For static stream applications where production and consumption rates of stages are close to compile-time constants, a compiler analysis is presented, which computes the minimum buffer capacity that maximizes the throughput. Based on this analysis, a new static streamscheduling algorithm is developed, which yields considerable speed-up and data delivery energy saving compared to a previous algorithm. For dynamic stream applications, I present a dynamically-sized array-based queue design that achieves speed-up and data delivery energy saving compared to a linked-list based queue design.
The current embedded processors often do not satisfy increasingly demanding computation requirements of embedded applications within acceptable energy efficiency, whereas application-specific integrated circuits require excessive design costs. In the Stanford Elm project, it was identified that instruction and data delivery, not computation, dominate the energy consumption of embedded processors. Consequently, the energy efficiency of delivering instructions and data must be sufficiently improved to close the efficiency gap between application-specific integrated circuits and programmable embedded processors. This dissertation demonstrates that the compiler and run-time system can play a crucial role in improving the energy efficiency of delivering instructions and data. Regarding instruction delivery, I present a compiler algorithm that manages L0 instruction scratch-pad memories that reside between processor cores and L1 caches. Despite the lack of tags, the scratch-pad memories with our algorithm can achieve lower miss rates than caches with the same capacities, saving significant instruction delivery energy. Regarding data delivery, I present methods that minimize memory-space requirements for parallelizing stream applications, applications that are commonly found in the embedded domain. When stream applications are parallelized in pipelining, large enough buffers are required between pipeline stages to sustain the throughput (e.g., double buffering). For static stream applications where production and consumption rates of stages are close to compile-time constants, a compiler analysis is presented, which computes the minimum buffer capacity that maximizes the throughput. Based on this analysis, a new static streamscheduling algorithm is developed, which yields considerable speed-up and data delivery energy saving compared to a previous algorithm. For dynamic stream applications, I present a dynamically-sized array-based queue design that achieves speed-up and data delivery energy saving compared to a linked-list based queue design.
This book proposes novel memory hierarchies and software optimization techniques for the optimal utilization of memory hierarchies. It presents a wide range of optimizations, progressively increasing in the complexity of analysis and of memory hierarchies. The final chapter covers optimization techniques for applications consisting of multiple processes found in most modern embedded devices.
Memory Design Techniques for Low Energy Embedded Systems centers one of the most outstanding problems in chip design for embedded application. It guides the reader through different memory organizations and technologies and it reviews the most successful strategies for optimizing them in the power and performance plane.
Energy-Aware Memory Management for Embedded Multimedia Systems: A Computer-Aided Design Approach presents recent computer-aided design (CAD) ideas that address memory management tasks, particularly the optimization of energy consumption in the memory subsystem. It explains how to efficiently implement CAD solutions, including theoretical methods an
Speed improvements in memory systems have not kept pace with the speed improvements of processors, leading to embedded systems whose performance is limited by the memory. This book presents design techniques for fast, energy-efficient and timing-predictable memory systems that achieve high performance and low energy consumption. In addition, the use of scratchpad memories significantly improves the timing predictability of the entire system, leading to tighter worst case execution time bounds.
Modern consumers carry many electronic devices, like a mobile phone, digital camera, GPS, PDA and an MP3 player. The functionality of each of these devices has gone through an important evolution over recent years, with a steep increase in both the number of features as in the quality of the services that they provide. However, providing the required compute power to support (an uncompromised combination of) all this functionality is highly non-trivial. Designing processors that meet the demanding requirements of future mobile devices requires the optimization of the embedded system in general and of the embedded processors in particular, as they should strike the correct balance between flexibility, energy efficiency and performance. In general, a designer will try to minimize the energy consumption (as far as needed) for a given performance, with a sufficient flexibility. However, achieving this goal is already complex when looking at the processor in isolation, but, in reality, the processor is a single component in a more complex system. In order to design such complex system successfully, critical decisions during the design of each individual component should take into account effect on the other parts, with a clear goal to move to a global Pareto optimum in the complete multi-dimensional exploration space. In the complex, global design of battery-operated embedded systems, the focus of Ultra-Low Energy Domain-Specific Instruction-Set Processors is on the energy-aware architecture exploration of domain-specific instruction-set processors and the co-optimization of the datapath architecture, foreground memory, and instruction memory organisation with a link to the required mapping techniques or compiler steps at the early stages of the design. By performing an extensive energy breakdown experiment for a complete embedded platform, both energy and performance bottlenecks have been identified, together with the important relations between the different components. Based on this knowledge, architecture extensions are proposed for all the bottlenecks.
Energy-Aware Memory Management for Embedded Multimedia Systems: A Computer-Aided Design Approach presents recent computer-aided design (CAD) ideas that address memory management tasks, particularly the optimization of energy consumption in the memory subsystem. It explains how to efficiently implement CAD solutions, including theoretical methods and novel algorithms. The book covers various energy-aware design techniques, including data-dependence analysis techniques, memory size estimation methods, extensions of mapping approaches, and memory banking approaches. It shows how these techniques are used to evaluate the data storage of an application, reduce dynamic and static energy consumption, design energy-efficient address generation units, and much more. Providing an algebraic framework for memory management tasks, this book illustrates how to optimize energy consumption in memory subsystems using CAD solutions. The algorithmic style of the text should help electronic design automation (EDA) researchers and tool developers create prototype software tools for system-level exploration, with the goal to ultimately obtain an optimized architectural solution of the memory subsystem.
One of the most important considerations in the product life-cycle of an embedded project is to understand and optimize the power consumption of the device. Power consumption is highly visible for hand-held devices which require battery power to be able to guarantee certain minimum usage/idle times between recharging. Other main embedded applications, such as medical equipment, test, measurement, media, and wireless base stations, are very sensitive to power as well – due to the need to manage the heat dissipation of increasingly powerful processors, power supply cost, and energy consumption cost – so the fact is that power consumption cannot be overlooked. The responsibility for setting and keeping power requirements often falls on the shoulders of hardware designers, but the software programmer has the ability to provide a large contribution to power optimization. Often, the impact that the software engineer has on the power consumption of a device is overlooked or underestimated. The goal of this chapter is to discuss how software can be used to optimize power consumption, starting with the basics of what power consumption consists of, how to properly measure power consumption, and then moving on to techniques for minimizing power consumption in software at the algorithmic level, hardware level, and data-flow level. This will include demonstrations of the various techniques and explanations of both how and why certain methods are effective at reducing power so the reader can take and apply this work to their application immediately.
This book provides its readers with the means to implement energy-efficient video systems, by using different optimization approaches at multiple abstraction levels. The authors evaluate the complete video system with a motive to optimize its different software and hardware components in synergy, increase the throughput-per-watt, and address reliability issues. Subsequently, this book provides algorithmic and architectural enhancements, best practices and deployment models for new video systems, while considering new implementation paradigms of hardware accelerators, parallelism for heterogeneous multi- and many-core systems, and systems with long life-cycles. Particular emphasis is given to the current video encoding industry standard H.264/AVC, and one of the latest video encoders (High Efficiency Video Coding, HEVC).