Power Management for Deep Submicron Microprocessors

As VLSI technology scales, the enhanced performance of smaller transistors comes at the expense of increased power consumption. In addition to the dynamic power consumed by the circuits there is a tremendous increase in the leakage power consumption which is further exacerbated by the increasing operating temperatures. The total power consumption of modern processors is distributed between the processor core, memory and interconnects. In this research two novel power management techniques are presented targeting the functional units and the global interconnects. First, since most leakage control schemes for processor functional units are based on circuit level techniques, such schemes inherently lack information about the operational profile of higher-level components of the system. This is a barrier to the pivotal task of predicting standby time. Without this prediction, it is extremely difficult to assess the value of any leakage control scheme. Consequently, a methodology that can predict the standby time is highly beneficial in bridging the gap between the information available at the application level and the circuit implementations. In this work, a novel Dynamic Sleep Signal Generator (DSSG) is presented. It utilizes the usage traces extracted from cycle accurate simulations of benchmark programs to predict the long standby periods associated with the various functional units. The DSSG bases its decisions on the current and previous standby state of the functional units to accurately predict the length of the next standby period. The DSSG presents an alternative to Static Sleep Signal Generation (SSSG) based on static counters that trigger the generation of the sleep signal when the functional units idle for a prespecified number of cycles. The test results of the DSSG are obtained by the use of a modified RISC superscalar processor, implemented by SimpleScalar, the most widely accepted open source vehicle for architectural analysis. In addition, the results are further verified by a Simultaneous Multithreading simulator implemented by SMTSIM. Leakage saving results shows an increase of up to 146% in leakage savings using the DSSG versus the SSSG, with an accuracy of 60-80% for predicting long standby periods. Second, chip designers in their effort to achieve timing closure, have focused on achieving the lowest possible interconnect delay through buffer insertion and routing techniques. This approach, though, taxes the power budget of modern ICs, especially those intended for wireless applications. Also, in order to achieve more functionality, die sizes are constantly increasing. This trend is leading to an increase in the average global interconnect length which, in turn, requires more buffers to achieve timing closure. Unconstrained buffering is bound to adversely affect the overall chip performance, if the power consumption is added as a major performance metric. In fact, the number of global interconnect buffers is expected to reach hundreds of thousands to achieve an appropriate timing closure. To mitigate the impact of the power consumed by the interconnect buffers, a power-efficient multi-pin routing technique is proposed in this research. The problem is based on a graph representation of the routing possibilities, including buffer insertion and identifying the least power path between the interconnect source and set of sinks. The novel multi-pin routing technique is tested by applying it to the ISPD and IBM benchmarks to verify the accuracy, complexity, and solution quality. Results obtained indicate that an average power savings as high as 32% for the 130-nm technology is achieved with no impact on the maximum chip frequency

Embedded System Design

A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues

System Synthesis for Embedded Multiprocessors

Modern embedded systems must increasingly accommodate dynamically changing operating environments, high computational requirements, and tight time-to-market windows. Such trends and the ever-increasing design complexity of embedded systems have challenged designers to raise the level of abstraction and replace traditional ad-hoc approaches with more efficient synthesis techniques. Additionally, since embedded multiprocessor systems are typically designed as final implementations for dedicated functions, modifications to embedded system implementations are rare, and this allows embedded system designers to spend significantly larger amounts of time to optimize the architecture and the employed software. This dissertation presents several system-level synthesis algorithms that employ time-intensive optimization techniques that allow the designer to explore a significantly larger part of the design space. It looks at critical issues that are at the core of the synthesis process --- selecting the architecture, partitioning the functionality over the components of the architecture, and scheduling activities such that design constraints and optimization objectives are satisfied. More specifically for the scheduling step, a new solution to the two-step multiprocessor scheduling problem is proposed. For the first step of clustering a highly efficient genetic algorithm is proposed. Several techniques for the second step of merging are proposed and finally a complete two-step effective solution is presented. Also, a randomization technique is applied to existing deterministic techniques to extend these techniques so that they can utilize arbitrary increases in available optimization time. This novel framework for extending deterministic algorithms in our context allows for accurate and fair comparison of our techniques against the state of the art. To further generalize the proposed clustering-based scheduling approach, a complementary two-step multiprocessor scheduling approach for heterogeneous multiprocessor systems is presented. This work is amongst the first works that formally studies the application of clustering to heterogeneous system scheduling. Several techniques are proposed and compared and conclusive results are presented. A modular system-level synthesis framework is then proposed. It synthesizes multi-mode, multi-task embedded systems under a number of hard constraints; optimizes a comprehensive set of objectives; and provides a set of alternative trade-off points in a given multi-objective design evaluation space. An extension of the framework is proposed to better address DVS, memory optimization, and efficient mappings onto dynamically reconfigurable hardware. An integrated framework for energy-driven scheduling onto embedded multiprocessor systems is proposed. It employs a solution representation that encodes both task assignment and ordering into a single chromosome and hence significantly reduces the search space and problem complexity. It is shown that a task assignment and scheduling that result in better performance do not necessarily save power, and hence, integrating task scheduling and voltage scheduling is crucial for fully exploiting the energy-saving potential of an embedded multiprocessor implementation

Hardware / Software Architectural and Technological Exploration for Energy-Efficient and Reliable Biomedical Devices

Nowadays, the ubiquity of smart appliances in our everyday lives is increasingly strengthening the links between humans and machines. Beyond making our lives easier and more convenient, smart devices are now playing an important role in personalized healthcare delivery. This technological breakthrough is particularly relevant in a world where population aging and unhealthy habits have made non-communicable diseases the first leading cause of death worldwide according to international public health organizations. In this context, smart health monitoring systems termed Wireless Body Sensor Nodes (WBSNs), represent a paradigm shift in the healthcare landscape by greatly lowering the cost of long-term monitoring of chronic diseases, as well as improving patients' lifestyles. WBSNs are able to autonomously acquire biological signals and embed on-node Digital Signal Processing (DSP) capabilities to deliver clinically-accurate health diagnoses in real-time, even outside of a hospital environment. Energy efficiency and reliability are fundamental requirements for WBSNs, since they must operate for extended periods of time, while relying on compact batteries. These constraints, in turn, impose carefully designed hardware and software architectures for hosting the execution of complex biomedical applications. In this thesis, I develop and explore novel solutions at the architectural and technological level of the integrated circuit design domain, to enhance the energy efficiency and reliability of current WBSNs. Firstly, following a top-down approach driven by the characteristics of biomedical algorithms, I perform an architectural exploration of a heterogeneous and reconfigurable computing platform devoted to bio-signal analysis. By interfacing a shared Coarse-Grained Reconfigurable Array (CGRA) accelerator, this domain-specific platform can achieve higher performance and energy savings, beyond the capabilities offered by a baseline multi-processor system. More precisely, I propose three CGRA architectures, each contributing differently to the maximization of the application parallelization. The proposed Single, Multi and Interleaved-Datapath CGRA designs allow the developed platform to achieve substantial energy savings of up to 37%, when executing complex biomedical applications, with respect to a multi-core-only platform. Secondly, I investigate how the modeling of technology reliability issues in logic and memory components can be exploited to adequately adjust the frequency and supply voltage of a circuit, with the aim of optimizing its computing performance and energy efficiency. To this end, I propose a novel framework for workload-dependent Bias Temperature Instability (BTI) impact analysis on biomedical application results quality. Remarkably, the framework is able to determine the range of safe circuit operating frequencies without introducing worst-case guard bands. Experiments highlight the possibility to safely raise the frequency up to 101% above the maximum obtained with the classical static timing analysis. Finally, through the study of several well-known biomedical algorithms, I propose an approach allowing energy savings by dynamically and unequally protecting an under-powered data memory in a new way compared to regular error protection schemes. This solution relies on the Dynamic eRror compEnsation And Masking (DREAM) technique that reduces by approximately 21% the energy consumed by traditional error correction codes

Optimizing Constrainted Concurrent Applications at Run-time

Computer systems are resource constrained. Application adaptation is a useful way to optimize system resource usage while satisfying an application’s performance requirements. Current multicore computer systems supporting these applications, however, are not designed to reliably meet these requirements. Meanwhile, these computer systems are resource-limited, e.g., have power-induced energy and thermal constraints. Compounding the application’s performance requirements are increasingly-stringent microprocessor thermal constraints. Previous application adaptation efforts, however, were ad-hoc, time-consuming, and highly application-specific, with limited portability between computer systems. This thesis presents OCCAM, a software platform for developing multicore adaptable applications. OCCAM’s design-time platform consists of design patterns, APIs, and data structures that allow application developers to specify the performance constraints and application-specific optimization techniques. OCCAM generates a run-time controller offline, using profiling data. It then uses this profiling data to generate an internal model that it subsequently employs to generate a robust Markov Decision Process-based Model Predictive Controller. Using a set of Recognition, Mining, and Synthesis benchmarks, the experimental study demonstrates that OCCAM can successfully optimize the system while meeting the systems performance requirements across a wide range of computer platforms, ranging from an energy-constrained single-core system to a high-performance 16-core system. Finally, OCCAM presents a simulation-based, stochastic model checking-based framework for quantifying the robustness of the controller