Hardware Software Techniques For Memory Power Optimizations In Embedded Processors

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.

To the hard-pressed systems designer this book will come as a godsend. It is a hands-on guide to the many ways in which processor-based systems are designed to allow low power devices. Covering a huge range of topics, and co-authored by some of the field’s top practitioners, the book provides a good starting point for engineers in the area, and to research students embarking upon work on embedded systems and architectures.

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 focuses on source-to-source code transformations that remove addressing-related overhead present in most multimedia or signal processing application programs. This approach is complementary to existing compiler technology. What is particularly attractive about the transformation flow pre sented here is that its behavior is nearly independent of the target processor platform and the underlying compiler. Hence, the different source code trans formations developed here lead to impressive performance improvements on most existing processor architecture styles, ranging from RISCs like ARM7 or MIPS over Superscalars like Intel-Pentium, PowerPC, DEC-Alpha, Sun and HP, to VLIW DSPs like TI C6x and Philips TriMedia. The source code did not have to be modified between processors to obtain these results. Apart from the performance improvements, the estimated energy is also significantly reduced for a given application run. These results were not obtained for academic codes but for realistic and rep resentative applications, all selected from the multimedia domain. That shows the industrial relevance and importance of this research. At the same time, the scientific novelty and quality of the contributions have lead to several excellent papers that have been published in internationally renowned conferences like e. g. DATE. This book is hence of interest for academic researchers, both because of the overall description of the methodology and related work context and for the detailed descriptions of the compilation techniques and algorithms.

Code optimization is a critical step in the development process as it directly impacts the ability of the system to do its intended job. Code that executes faster means more channels, more work performed and competitive advantage. Code that executes in less memory enables more application features to fit into the cell phone. Code that executes with less overall power consumption increases battery life or reduces money spent on powering a base station. This chapter is intended to help programmers write the most efficient code possible, whether that is measured in processor cycles, memory, or power. It starts with an introduction to using the tool chain, covers the importance of knowing the embedded architecture before optimization, then moves on to cover a wide range of optimization techniques. Techniques are presented which are valid on all programmable architectures – C-language optimization techniques and general loop transformations. Real-world examples are presented throughout.

Embedded systems are everywhere in contemporary life and are supposed to make our lives more comfortable. In industry, embedded systems are used to manage and control complex systems (e.g. nuclear power plants, telecommunications and flight control) and they are also taking an important place in our daily activities (e.g. smartphones, security alarms and traffic lights). In the design of embedded systems, memory allocation and data assignment are among the main challenges that electronic designers have to face. In fact, they impact heavily on the main cost metrics (power consumption, performance and area) in electronic devices. Thus designers of embedded systems have to pay careful attention in order to minimize memory requirements, thus improving memory throughput and limiting the power consumption by the system’s memory. Electronic designers attempt to minimize memory requirements with the aim of lowering the overall system costs. A state of the art of optimization techniques for memory management and data assignment is presented in this book.

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

The multicore revolution has reached the deployment stage in embedded systems ranging from small ultramobile devices to large telecommunication servers. The transition from single to multicore processors, motivated by the need to increase performance while conserving power, has placed great responsibility on the shoulders of software engineers. In this new embedded multicore era, the toughest task is the development of code to support more sophisticated systems. This book provides embedded engineers with solid grounding in the skills required to develop software targeting multicore processors. Within the text, the author undertakes an in-depth exploration of performance analysis, and a close-up look at the tools of the trade. Both general multicore design principles and processor-specific optimization techniques are revealed. Detailed coverage of critical issues for multicore employment within embedded systems is provided, including the Threading Development Cycle, with discussions of analysis, design, development, debugging, and performance tuning of threaded applications. Software development techniques engendering optimal mobility and energy efficiency are highlighted through multiple case studies, which provide practical “how-to advice on implementing the latest multicore processors. Finally, future trends are discussed, including terascale, speculative multithreading, transactional memory, interconnects, and the software-specific implications of these looming architectural developments. This is the only book to explain software optimization for embedded multi-core systems Helpful tips, tricks and design secrets from an Intel programming expert, with detailed examples using the popular X86 architecture Covers hot topics, including ultramobile devices, low-power designs, Pthreads vs. OpenMP, and heterogeneous cores

Modern embedded systems deploy several hardware accelerators, in a heterogeneous manner, to deliver high-performance computing. Among such devices, graphics processing units (GPUs) have earned a prominent position by virtue of their immense computing power. However, a system design that relies on sheer throughput of GPUs is often incapable of satisfying the strict power- and time-related constraints faced by the embedded systems. This thesis presents several system-level software techniques to optimize the design of GPU-based embedded systems under various graphics and non-graphics applications. As compared to the conventional application-level optimizations, the system-wide view of our proposed techniques brings about several advantages: First, it allows for fully incorporating the limitations and requirements of the various system parts in the design process. Second, it can unveil optimization opportunities through exposing the information flow between the processing components. Third, the techniques are generally applicable to a wide range of applications with similar characteristics. In addition, multiple system-level techniques can be combined together or with application-level techniques to further improve the performance. We begin by studying some of the unique attributes of GPU-based embedded systems and discussing several factors that distinguish the design of these systems from that of the conventional high-end GPU-based systems. We then proceed to develop two techniques that address an important challenge in the design of GPU-based embedded systems from different perspectives. The challenge arises from the fact that GPUs require a large amount of workload to be present at runtime in order to deliver a high throughput. However, for some embedded applications, collecting large batches of input data requires an unacceptable waiting time, prompting a trade-off between throughput and latency. We also develop an optimization technique for GPU-based applications to address the memory bottleneck issue by utilizing the GPU L2 cache to shorten data access time. Moreover, in the area of graphics applications, and in particular with a focus on mobile games, we propose a power management scheme to reduce the GPU power consumption by dynamically adjusting the display resolution, while considering the user's visual perception at various resolutions. We also discuss the collective impact of the proposed techniques in tackling the design challenges of emerging complex systems. The proposed techniques are assessed by real-life experimentations on GPU-based hardware platforms, which demonstrate the superior performance of our approaches as compared to the state-of-the-art techniques.