Using data compression for increasing memory system utilization

Cataloged from PDF version of article.The memory system presents one of the critical challenges in embedded system design and optimization. This is mainly due to the ever-increasing code complexity of embedded applications and the exponential increase seen in the amount of data they manipulate. The memory bottleneck is even more important for multiprocessor-system-on-a-chip (MPSoC) architectures due to the high cost of off-chip memory accesses in terms of both energy and performance. As a result, reducing the memory-space occupancy of embedded applications is very important and will be even more important in the next decade. While it is true that the on-chip memory capacity of embedded systems is continuously increasing, the increases in the complexity of embedded applications and the sizes of the data sets they process are far greater. Motivated by this observation, this paper presents and evaluates a compiler-driven approach to data compression for reducing memory-space occupancy. Our goal is to study how automated compiler support can help in deciding the set of data elements to compress/decompress and the points during execution at which these compressions/decompressions should be performed. We first study this problem in the context of single-core systems and then extend it to MPSoCs where we schedule compressions and decompressions intelligently such that they do not conflict with application execution as much as possible. Particularly, in MPSoCs, one needs to decide which processors should participate in the compression and decompression activities at any given point during the course of execution. We propose both static and dynamic algorithms for this purpose. In the static scheme, the processors are divided into two groups: those performing compression/decompression and those executing the application, and this grouping is maintained throughout the execution of the application. In the dynamic scheme, on the other hand, the execution starts with some grouping but this grouping can change during the course of execution, depending on the dynamic variations in the data access pattern. Our experimental results show that, in a single-core system, the proposed approach reduces maximum memory occupancy by 47.9% and average memory occupancy by 48.3% when averaged over all the benchmarks. Our results also indicate that, in an MPSoC, the average energy saving is 12.7% when all eight benchmarks are considered. While compressions and decompressions and related bookkeeping activities take extra cycles and memory space and consume additional energy, we found that the improvements they bring from the memory space, execution cycles, and energy perspectives are much higher than these overheads

Performance and Memory Space Optimizations for Embedded Systems

Embedded systems have three common principles: real-time performance, low power consumption, and low price (limited hardware). Embedded computers use chip multiprocessors (CMPs) to meet these expectations. However, one of the major problems is lack of efficient software support for CMPs; in particular, automated code parallelizers are needed. The aim of this study is to explore various ways to increase performance, as well as reducing resource usage and energy consumption for embedded systems. We use code restructuring, loop scheduling, data transformation, code and data placement, and scratch-pad memory (SPM) management as our tools in different embedded system scenarios. The majority of our work is focused on loop scheduling. Main contributions of our work are: We propose a memory saving strategy that exploits the value locality in array data by storing arrays in a compressed form. Based on the compressed forms of the input arrays, our approach automatically determines the compressed forms of the output arrays and also automatically restructures the code. We propose and evaluate a compiler-directed code scheduling scheme, which considers both parallelism and data locality. It analyzes the code using a locality parallelism graph representation, and assigns the nodes of this graph to processors.We also introduce an Integer Linear Programming based formulation of the scheduling problem. We propose a compiler-based SPM conscious loop scheduling strategy for array/loop based embedded applications. The method is to distribute loop iterations across parallel processors in an SPM-conscious manner. The compiler identifies potential SPM hits and misses, and distributes loop iterations such that the processors have close execution times. We present an SPM management technique using Markov chain based data access. We propose a compiler directed integrated code and data placement scheme for 2-D mesh based CMP architectures. Using a Code-Data Affinity Graph (CDAG) to represent the relationship between loop iterations and array data, it assigns the sets of loop iterations to processing cores and sets of data blocks to on-chip memories. We present a memory bank aware dynamic loop scheduling scheme for array intensive applications.The goal is to minimize the number of memory banks needed for executing the group of loop iterations

A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

05141 Abstracts Collection -- Power-aware Computing Systems

From 03.04.05 to 08.04.05, the Dagstuhl Seminar 05141 ``Power-aware Computing Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and discussed open problems. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are collected in this paper. The first section describes the seminar topics and goals. Links to extended abstracts or full papers are provided, if available

The Impact of Process Scaling on Scratchpad Memory Energy Savings

Scratchpad memories have been shown to reduce power consumption, but the different characteristics of nanometer scale processes, such as increased leakage power, motivate an examination of how the benefits of these memories change with process scaling. Process and application characteristics affect the amount of energy saved by a scratchpad memory. Increases in leakage as a percentage of total power particularly impact applications that rarely access memory. This study examines how the benefits of scratchpad memories have changed in newer processes, based on the measured performance of the WIMS (Wireless Integrated MicroSystems) microcontroller implemented in 180- and 65-nm processes and upon simulations of this microcontroller implemented in a 32-nm process. The results demonstrate that scratchpad memories will continue to improve the power dissipation of many applications, given the leakage anticipated in the foreseeable future

Coarse-grained reconfigurable array architectures

Coarse-Grained Reconfigurable Array (CGRA) architectures accelerate the same inner loops that benefit from the high ILP support in VLIW architectures. By executing non-loop code on other cores, however, CGRAs can focus on such loops to execute them more efficiently. This chapter discusses the basic principles of CGRAs, and the wide range of design options available to a CGRA designer, covering a large number of existing CGRA designs. The impact of different options on flexibility, performance, and power-efficiency is discussed, as well as the need for compiler support. The ADRES CGRA design template is studied in more detail as a use case to illustrate the need for design space exploration, for compiler support and for the manual fine-tuning of source code

Pairing Software-Managed Caching with Decay Techniques to Balance Reliability and Static Power in Next-Generation Caches

Since array structures represent well over half the area and transistors on-chip, maintaining their ability to scale is crucial for overall technology scaling. Shrinking transistor sizes are resulting in increased probabilities of single events causing single- and multi-bit upsets which require adoption of more complex and power hungry error detection and correction codes (ECC) in hardware. At the same time, SRAM leakage energy is increasing partly due to technology trends and partly due to the increasing number of transistors present. This paper proposes and evaluates methods of reducing the static power requirements of caches, while also maintaining high reliability. In particular, we propose methods of applying reduced ECC techniques to data that has been identified (by programmer or compiler) as error-tolerant. This segregation, in turn, makes both the default data and the error-tolerant data more amenable to decay-based techniques for leakage control. We examine the potential of this split memory hierarchy along several dimensions. In particular, we consider the power and reliability issues inherent in the approach. Overall, we show that our approach allows the ECC requirements of future applications and caches to be met while also reducing leakage energy

Doctor of Philosophy

dissertationAdvancements in process technology and circuit techniques have enabled the creation of small chemical microsystems for use in a wide variety of biomedical and sensing applications. For applications requiring a small microsystem, many components can be integrated onto a single chip. This dissertation presents many low-power circuits, digital and analog, integrated onto a single chip called the Utah Microcontroller. To guide the design decisions for each of these components, two specific microsystems have been selected as target applications: a Smart Intravaginal Ring (S-IVR) and an NO releasing catheter. Both of these applications share the challenging requirements of integrating a large variety of low-power mixed-signal circuitry onto a single chip. These applications represent the requirements of a broad variety of small low-power sensing systems. In the course of the development of the Utah Microcontroller, several unique and significant contributions were made. A central component of the Utah Microcontroller is the WIMS Microprocessor, which incorporates a low-power feature called a scratchpad memory. For the first time, an analysis of scaling trends projected that scratchpad memories will continue to save power for the foreseeable future. This conclusion was bolstered by measured data from a fabricated microcontroller. In a 32 nm version of the WIMS Microprocessor, the scratchpad memory is projected to save ~10-30% of memory access energy depending upon the characteristics of the embedded program. Close examination of application requirements informed the design of an analog-to-digital converter, and a unique single-opamp buffered charge scaling DAC was developed to minimize power consumption. The opamp was designed to simultaneously meet the varied demands of many chip components to maximize circuit reuse. Each of these components are functional, have been integrated, fabricated, and tested. This dissertation successfully demonstrates that the needs of emerging small low-power microsystems can be met in advanced process nodes with the incorporation of low-power circuit techniques and design choices driven by application requirements

Hardware/software approaches for reducing the process variation impact on instruction fetches

Cataloged from PDF version of article.As technology moves towards finer process geometries, it is becoming extremely difficult to control critical physical parameters such as channel length, gate oxide thickness, and dopant ion concentration. Variations in these parameters lead to dramatic variations in access latencies in Static Random Access Memory (SRAM) devices. This means that different lines of the same cache may have different access latencies. A simple solution to this problem is to adopt the worst-case latency paradigm. While this egalitarian cache management is simple, it may introduce significant performance overhead during instruction fetches when both address translation (instruction Translation Lookaside Buffer (TLB) access) and instruction cache access take place, making this solution infeasible for future high-performance processors. In this study, we first propose some hardware and software enhancements and then, based on those, investigate several techniques to mitigate the effect of process variation on the instruction fetch pipeline stage in modern processors. For address translation, we study an approach that performs the virtual-to-physical page translation once, then stores it in a special register, reusing it as long as the execution remains on the same instruction page. To handle varying access latencies across different instruction cache lines, we annotate the cache access latency of instructions within themselves to give the circuitry a hint about how long to wait for the next instruction to become available