내장형 프로세서에서의 코드 크기 최적화를 위한 아키텍처 설계 및 컴파일러 지원

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 백윤흥.Embedded processors usually need to satisfy very tight design constraints to achieve low power consumption, small chip area, and high performance. One of the obstacles to meeting these requirements is related to delivering instructions from instruction memory/caches. The size of instruction memory/cache considerably contributes total chip area. Further, frequent access to caches incurs high power/energy consumption and significantly hampers overall system performance due to cache misses. To reduce the negative effects of the instruction delivery, therefore, this study focuses on the sizing of instruction memory/cache through code size optimization. One observation for code size optimization is that very long instruction word (VLIW) architectures often consume more power and memory space than necessary due to long instruction bit-width. One way to lessen this problem is to adopt a reduced bit-width ISA (Instruction Set Architecture) that has a narrower instruction word length. In practice, however, it is impossible to convert a given ISA fully into an equivalent reduced bit-width one because the narrow instruction word, due to bitwidth restrictions, can encode only a small subset of normal instructions in the original ISA. To explore the possibility of complete conversion of an existing 32-bit ISA into a 16-bit one that supports effectively all 32-bit instructions, we propose the reduced bit-width (e.g. 16-bit × 4-way) VLIW architectures that equivalently behave as their original bit-width (e.g. 32-bit × 4-way) architectures with the help of dynamic implied addressing mode (DIAM). Second, we observe that code duplication techniques have been proposed to increase the reliability against soft errors in multi-issue embedded systems such as VLIW by exploiting empty slots for duplicated instructions. Unfortunately, all duplicated instructions cannot be allocated to empty slots, which enforces generating additional VLIW packets to include the duplicated instructions. The increase of code size due to the extra VLIW packets is necessarily accompanied with the enhanced reliability. In order to minimize code size, we propose a novel approach compiler-assisted dynamic code duplication scheme, which accepts an assembly code composed of only original instructions as input, and generates duplicated instructions at runtime with the help of encoded information attached to original instructions. Since the duplicates of original instructions are not explicitly present in the assembly code, the increase of code size due to the duplicated instructions can be avoided in the proposed scheme. Lastly, the third observation is that, to cope with soft errors similarly to the second observation, a recently proposed software-based technique with TMR (Triple Modular Redundancy) implemented on coarse-grained reconfigurable architectures (CGRA) incurs the increase of configuration size, which is corresponding to the code size of CGRA, and thus extreme overheads in terms of runtime and energy consumption mainly due to expensive voting mechanisms for the outputs from the triplication of every operation. To reduce the expensive performance overhead due to the large configuration from the validation mechanism, we propose selective validation mechanisms for efficient modular redundancy techniques in the datapath on CGRA. The proposed techniques selectively validate the results at synchronous operations rather than every operation.Abstract i Chapter 1 Introduction 1 1.1 Instruction Delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 The causes of code size increase . . . . . . . . . . . . . . . . . . . . 2 1.2.1 Instruction Bit-width in VLIW Architectures . . . . . . . . . 2 1.2.2 Instruction Redundancy . . . . . . . . . . . . . . . . . . . . 3 Chapter 2 Reducing Instruction Bit-width with Dynamic Implied Addressing Mode (DIAM) 7 2.1 Conceptual View . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Architecture Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.1 ISA Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.2 Remote Operand Array Buffer . . . . . . . . . . . . . . . . . 15 2.2.3 Microarchitecture . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3 Compiler Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.1 16-bit Instruction Generation . . . . . . . . . . . . . . . . . . 24 2.3.2 DDG Construction & Scheduling . . . . . . . . . . . . . . . 26 2.4 VLES(Variable Length Execution Set) Architecture with a Reduced Bit-width Instruction Set . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.1 Architecture Design . . . . . . . . . . . . . . . . . . . . . . 30 2.4.2 Compiler Support . . . . . . . . . . . . . . . . . . . . . . . . 34 2.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.5.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.5.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . 48 2.6 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Chapter 3 Compiler-assisted Dynamic Code Duplication Scheme for Soft Error Resilient VLIW Architectures 53 3.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.2 Compiler-assisted Dynamic Code Duplication . . . . . . . . . . . . . 58 3.2.1 ISA Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.2.2 Modified Fetch Stage . . . . . . . . . . . . . . . . . . . . . . 62 3.3 Compilation Techniques . . . . . . . . . . . . . . . . . . . . . . . . 66 3.3.1 Static Code Duplication Algorithm . . . . . . . . . . . . . . 67 3.3.2 Vulnerability-aware Duplication Algorithm . . . . . . . . . . 68 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . 71 3.4.2 Effectiveness of Compiler-assisted Dynamic Code Duplication 73 3.4.3 Effectiveness of Vulnerability-aware Duplication Algorithm . 77 Chapter 4 Selective Validation Techniques for Robust CGRAs against Soft Errors 85 4.1 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.3 Our Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 4.3.1 Selective Validation Mechanism . . . . . . . . . . . . . . . . 91 4.3.2 Compilation Flow and Performance Analysis . . . . . . . . . 92 4.3.3 Fault Coverage Analysis . . . . . . . . . . . . . . . . . . . . 96 4.3.4 Our Optimization - Minimizing Store Operation . . . . . . . . 97 4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.4.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . 100 Chapter 5 Conculsion 110 초록 122Docto

State of the art baseband DSP platforms for Software Defined Radio: A survey

Software Defined Radio (SDR) is an innovative approach which is becoming a more and more promising technology for future mobile handsets. Several proposals in the field of embedded systems have been introduced by different universities and industries to support SDR applications. This article presents an overview of current platforms and analyzes the related architectural choices, the current issues in SDR, as well as potential future trends.Peer reviewe

Design and Implementation of MIMO OFDM IEEE802.11n Receiver Blocks on Heterogeneous Multicore Architecture

In this thesis, the performance of a heterogeneous multicore platform in terms of technical capability is evaluated. Therefore, the choice of architecture in general can be based on a set of diverse applications. Selected applications can be parallel or serial in nature. Applications evaluation are often based on various performance metrics including the resource utilization and execution time. The wireless communication systems are expanded to accelerate their functions execution in both software and hardware. The embedded systems which involve several types of communication systems perform a large number of computations which require short execution time and minimized power consumption. Also, there is a growing demand for application-specific accelerators aiding general-purpose. One feasible way is to use heterogeneous multi-core platforms. Furthermore, many application-specific accelerators are loosely connected with each other. In this study, the implementation of Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) receiver is evaluated by applying a Heterogeneous Multicore Architecture (HMA). The MIMO OFDM receiver is composed of computationally intensive and general-purpose processing tasks and can serve maximum coverage for evaluation of the HMA. The receiver blocks are designed by crafting template-based Coarse-grained Reconfigurable Array (CGRA) devices. In this case study, four streams (antennas) are proposed in order to process the data over CGRAs simultaneously. HMA nodes will be reconfigured at run-time in different blocks of the receiver. In this experimental work, according to the performance of each CGRA, the collective performance of the entire platform as well as NoC traffic is recorded considering the number of clock cycles and also several high-level performance criteria. The implementation of OFDM receiver scaled CGRAs to various dimensions. The data can also be exchanged between diverse nodes on the NoC structure by utilizing direct memory access (DMA) devices independently

RowCore: A Processing-Near-Memory Architecture for Big Data Machine Learning

The technology-push of die stacking and application-pull of Big Data machine learning (BDML) have created a unique opportunity for processing-near-memory (PNM). This paper makes four contributions: (1) While previous PNM work explores general MapReduce workloads, we identify three workload characteristics: (a) irregular-and-compute-light (i.e., perform only a few operations per input word which include data-dependent branches and indirect memory accesses); (b) compact (i.e., the computation has a small intermediate live data and uses only a small amount of contiguous input data); and (c) memory-row-dense (i.e., process the input data without skipping over many bytes). We show that BDMLs have or can be transformed to have these characteristics which, except for irregularity, are necessary for bandwidth- and energyefficient PNM, irrespective of the architecture. (2) Based on these characteristics, we propose RowCore, a row-oriented PNM architecture, which (pre)fetches and operates on entire memory rows to exploit BDMLs’ row-density. Instead of this row-centric access and compute-schedule, traditional architectures opportunistically improve row locality while fetching and operating on cache blocks. (3) RowCore employs well-known MIMD execution to handle BDMLs’ irregularity, and sequential prefetch of input data to hide memory latency. In RowCore, however, one corelet prefetches a row for all the corelets which may stray far from each other due to their MIMD execution. Consequently, a leading corelet may prematurely evict the prefetched data before a lagging corelet has consumed the data. RowCore employs novel cross-corelet flow-control to prevent such eviction. (4) RowCore further exploits its flow-controlled prefetch for frequency scaling based on novel coarse-grain compute-memory rate-matching which decreases (increases) the processor clock speed when the prefetch buffers are empty (full). Using simulations, we show that RowCore improves performance and energy, by 135% and 20% over a GPGPU with prefetch, and by 35% and 34% over a multicore with prefetch, when all three architectures use the same resources (i.e., number of cores, and on-processor-die memory) and identical diestacking (i.e., GPGPUs/multicores/RowCore and DRAM)

A fine-grained parallel dataflow-inspired architecture for streaming applications

Data driven streaming applications are quite common in modern multimedia and wireless applications, like for example video and audio processing. The main components of these applications are Digital Signal Processing (DSP) algorithms. These algorithms are not extremely complex in terms of their structure and the operations that make up the algorithms are fairly simple (usually binary mathematical operations like addition and multiplication). What makes it challenging to implement and execute these algorithms efficiently is their large degree of fine-grained parallelism and the required throughput. DSP algorithms can usually be described as dataflow graphs with nodes corresponding to operations and edges between the nodes expressing data dependencies. A node fires, i.e. executes, as soon as all required input data has arrived at its input edge(s). \ud \ud To execute DSP algorithms efficiently while maintaining flexibility, coarse-grained reconfigurable arrays (CGRAs) can be used. CGRAs are composed of a set of small, reconfigurable cores, interconnected in e.g. a two dimensional array. Each core by itself is not very powerful, yet the complete array of cores forms an efficient architecture with a high throughput due to its ability to efficiently execute operations in parallel. \ud \ud In this thesis, we present a CGRA targeted at data driven streaming DSP applications that contain a large degree of fine grained parallelism, such as matrix manipulations or filter algorithms. Along with the architecture, also a programming language is presented that can directly describe DSP applications as dataflow graphs which are then automatically mapped and executed on the architecture. In contrast to previously published work on CGRAs, the guiding principle and inspiration for the presented CGRA and its corresponding programming paradigm is the dataflow principle. \ud \ud The result of this work is a completely integrated framework targeted at streaming DSP algorithms, consisting of a CGRA, a programming language and a compiler. The complete system is based on dataflow principles. We conclude that by using an architecture that is based on dataflow principles and a corresponding programming paradigm that can directly express dataflow graphs, DSP algorithms can be implemented in a very intuitive and straightforward manner

Runtime Hardware Reconfiguration in Wireless Sensor Networks for Condition Monitoring

The integration of miniaturized heterogeneous electronic components has enabled the deployment of tiny sensing platforms empowered by wireless connectivity known as wireless sensor networks. Thanks to an optimized duty-cycled activity, the energy consumption of these battery-powered devices can be reduced to a level where several years of operation is possible. However, the processing capability of currently available wireless sensor nodes does not scale well with the observation of phenomena requiring a high sampling resolution. The large amount of data generated by the sensors cannot be handled efficiently by low-power wireless communication protocols without a preliminary filtering of the information relevant for the application. For this purpose, energy-efficient, flexible, fast and accurate processing units are required to extract important features from the sensor data and relieve the operating system from computationally demanding tasks. Reconfigurable hardware is identified as a suitable technology to fulfill these requirements, balancing implementation flexibility with performance and energy-efficiency. While both static and dynamic power consumption of field programmable gate arrays has often been pointed out as prohibitive for very-low-power applications, recent programmable logic chips based on non-volatile memory appear as a potential solution overcoming this constraint. This thesis first verifies this assumption with the help of a modular sensor node built around a field programmable gate array based on Flash technology. Short and autonomous duty-cycled operation combined with hardware acceleration efficiently drop the energy consumption of the device in the considered context. However, Flash-based devices suffer from restrictions such as long configuration times and limited resources, which reduce their suitability for complex processing tasks. A template of a dynamically reconfigurable architecture built around coarse-grained reconfigurable function units is proposed in a second part of this work to overcome these issues. The module is conceived as an overlay of the sensor node FPGA increasing the implementation flexibility and introducing a standardized programming model. Mechanisms for virtual reconfiguration tailored for resource-constrained systems are introduced to minimize the overhead induced by this genericity. The definition of this template architecture leaves room for design space exploration and application- specific customization. Nevertheless, this aspect must be supported by appropriate design tools which facilitate and automate the generation of low-level design files. For this purpose, a software tool is introduced to graphically configure the architecture and operation of the hardware accelerator. A middleware service is further integrated into the wireless sensor network operating system to bridge the gap between the hardware and the design tools, enabling remote reprogramming and scheduling of the hardware functionality at runtime. At last, this hardware and software toolchain is applied to real-world wireless sensor network deployments in the domain of condition monitoring. This category of applications often require the complex analysis of signals in the considered range of sampling frequencies such as vibrations or electrical currents, making the proposed system ideally suited for the implementation. The flexibility of the approach is demonstrated by taking examples with heterogeneous algorithmic specifications. Different data processing tasks executed by the sensor node hardware accelerator are modified at runtime according to application requests

Gestión de jerarquías de memoria híbridas a nivel de sistema

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadoras y Automática y de Ku Leuven, Arenberg Doctoral School, Faculty of Engineering Science, leída el 11/05/2017.In electronics and computer science, the term ‘memory’ generally refers to devices that are used to store information that we use in various appliances ranging from our PCs to all hand-held devices, smart appliances etc. Primary/main memory is used for storage systems that function at a high speed (i.e. RAM). The primary memory is often associated with addressable semiconductor memory, i.e. integrated circuits consisting of silicon-based transistors, used for example as primary memory but also other purposes in computers and other digital electronic devices. The secondary/auxiliary memory, in comparison provides program and data storage that is slower to access but offers larger capacity. Examples include external hard drives, portable flash drives, CDs, and DVDs. These devices and media must be either plugged in or inserted into a computer in order to be accessed by the system. Since secondary storage technology is not always connected to the computer, it is commonly used for backing up data. The term storage is often used to describe secondary memory. Secondary memory stores a large amount of data at lesser cost per byte than primary memory; this makes secondary storage about two orders of magnitude less expensive than primary storage. There are two main types of semiconductor memory: volatile and nonvolatile. Examples of non-volatile memory are ‘Flash’ memory (sometimes used as secondary, sometimes primary computer memory) and ROM/PROM/EPROM/EEPROM memory (used for firmware such as boot programs). Examples of volatile memory are primary memory (typically dynamic RAM, DRAM), and fast CPU cache memory (typically static RAM, SRAM, which is fast but energy-consuming and offer lower memory capacity per are a unit than DRAM). Non-volatile memory technologies in Si-based electronics date back to the 1990s. Flash memory is widely used in consumer electronic products such as cellphones and music players and NAND Flash-based solid-state disks (SSDs) are increasingly displacing hard disk drives as the primary storage device in laptops, desktops, and even data centers. The integration limit of Flash memories is approaching, and many new types of memory to replace conventional Flash memories have been proposed. The rapid increase of leakage currents in Silicon CMOS transistors with scaling poses a big challenge for the integration of SRAM memories. There is also the case of susceptibility to read/write failure with low power schemes. As a result of this, over the past decade, there has been an extensive pooling of time, resources and effort towards developing emerging memory technologies like Resistive RAM (ReRAM/RRAM), STT-MRAM, Domain Wall Memory and Phase Change Memory(PRAM). Emerging non-volatile memory technologies promise new memories to store more data at less cost than the expensive-to build silicon chips used by popular consumer gadgets including digital cameras, cell phones and portable music players. These new memory technologies combine the speed of static random-access memory (SRAM), the density of dynamic random-access memory (DRAM), and the non-volatility of Flash memory and so become very attractive as another possibility for future memory hierarchies. The research and information on these Non-Volatile Memory (NVM) technologies has matured over the last decade. These NVMs are now being explored thoroughly nowadays as viable replacements for conventional SRAM based memories even for the higher levels of the memory hierarchy. Many other new classes of emerging memory technologies such as transparent and plastic, three-dimensional(3-D), and quantum dot memory technologies have also gained tremendous popularity in recent years...En el campo de la informática, el término ‘memoria’ se refiere generalmente a dispositivos que son usados para almacenar información que posteriormente será usada en diversos dispositivos, desde computadoras personales (PC), móviles, dispositivos inteligentes, etc. La memoria principal del sistema se utiliza para almacenar los datos e instrucciones de los procesos que se encuentre en ejecución, por lo que se requiere que funcionen a alta velocidad (por ejemplo, DRAM). La memoria principal está implementada habitualmente mediante memorias semiconductoras direccionables, siendo DRAM y SRAM los principales exponentes. Por otro lado, la memoria auxiliar o secundaria proporciona almacenaje(para ficheros, por ejemplo); es más lenta pero ofrece una mayor capacidad. Ejemplos típicos de memoria secundaria son discos duros, memorias flash portables, CDs y DVDs. Debido a que estos dispositivos no necesitan estar conectados a la computadora de forma permanente, son muy utilizados para almacenar copias de seguridad. La memoria secundaria almacena una gran cantidad de datos aun coste menor por bit que la memoria principal, siendo habitualmente dos órdenes de magnitud más barata que la memoria primaria. Existen dos tipos de memorias de tipo semiconductor: volátiles y no volátiles. Ejemplos de memorias no volátiles son las memorias Flash (algunas veces usadas como memoria secundaria y otras veces como memoria principal) y memorias ROM/PROM/EPROM/EEPROM (usadas para firmware como programas de arranque). Ejemplos de memoria volátil son las memorias DRAM (RAM dinámica), actualmente la opción predominante a la hora de implementar la memoria principal, y las memorias SRAM (RAM estática) más rápida y costosa, utilizada para los diferentes niveles de cache. Las tecnologías de memorias no volátiles basadas en electrónica de silicio se remontan a la década de1990. Una variante de memoria de almacenaje por carga denominada como memoria Flash es mundialmente usada en productos electrónicos de consumo como telefonía móvil y reproductores de música mientras NAND Flash solid state disks(SSDs) están progresivamente desplazando a los dispositivos de disco duro como principal unidad de almacenamiento en computadoras portátiles, de escritorio e incluso en centros de datos. En la actualidad, hay varios factores que amenazan la actual predominancia de memorias semiconductoras basadas en cargas (capacitivas). Por un lado, se está alcanzando el límite de integración de las memorias Flash, lo que compromete su escalado en el medio plazo. Por otra parte, el fuerte incremento de las corrientes de fuga de los transistores de silicio CMOS actuales, supone un enorme desafío para la integración de memorias SRAM. Asimismo, estas memorias son cada vez más susceptibles a fallos de lectura/escritura en diseños de bajo consumo. Como resultado de estos problemas, que se agravan con cada nueva generación tecnológica, en los últimos años se han intensificado los esfuerzos para desarrollar nuevas tecnologías que reemplacen o al menos complementen a las actuales. Los transistores de efecto campo eléctrico ferroso (FeFET en sus siglas en inglés) se consideran una de las alternativas más prometedores para sustituir tanto a Flash (por su mayor densidad) como a DRAM (por su mayor velocidad), pero aún está en una fase muy inicial de su desarrollo. Hay otras tecnologías algo más maduras, en el ámbito de las memorias RAM resistivas, entre las que cabe destacar ReRAM (o RRAM), STT-RAM, Domain Wall Memory y Phase Change Memory (PRAM)...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu