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

A Hybrid Instruction Prefetching Mechanism for Ultra Low-Power Multicore Clusters

The instruction memory hierarchy plays a critical role in performance and energy efficiency of ultralow-power (ULP) processors for the Internet-of-Things (IoT) end-nodes. This is mainly due to the extremely tight power envelope and area budgets, which imply small instruction-caches (I-Cache) operating at very low supply voltages (near-threshold). The challenge is aggravated by the fact that multiple processors, fetching in parallel, require plenty of bandwidth from the I-Caches. In this letter, we propose a low-cost and energy efficient hybrid instruction-prefetching mechanism to be integrated with a ULP multicore cluster. We study its performance for a wide range of IoT applications, from cryptography to computer vision, and show that it can effectively improve the hit-rate of almost all of them to above 95% (average performance improvement of over 2 \times ). In addition, we designed our prefetcher and integrated it in a 4-cores cluster in 28 nm fully-depleted silicon-on-insulator (FDSOI) technology. We show that system's power consumption increases only by about 11% and silicon area by less than 1%. Altogether, a total energy reduction of 1.9x is achieved, thanks to more than 2x performance improvement, enabling a significantly longer battery life

Reducing instruction fetch energy with backwards branch control information and buffering

Many emerging applications, e.g. in the embedded and DSP space, are often characterized by their loopy nature where a substantial part of the execution time is spent within a few program phases. Loop buffering techniques have been proposed for capturing and processing these loops in small buffers to reduce the processor‘s instruction fetch energy. However, these schemes are limited to straight-line or inner-most loops and fail to adequately handle complex loops. In this paper, we propose a dynamic loop buffering mech-anism that uses backwards branch control information to identify, capture and process complex loop structures. The DLB controller has been fully implemented in VHDL, syn-thesized and timed with the IBM Booledozer and Einstimer Synthesis tools, and analyzed for power with the Sequence PowerTheater tool. Our experiments show that the DLB approach, on average, results in a factor of 3 reduction in energy consumption compared to a traditional instruction memory design at an area overhead of about 9%

Fast, predictable and low energy memory references through architecture-aware compilation

The design of future high-performance embedded systems is hampered by two problems: First, the required hardware needs more energy than is available from batteries. Second, current cache-based approaches for bridging the increasing speed gap between processors and memories cannot guarantee predictable real-time behavior. A contribution to solving both problems is made in this paper which describes a comprehensive set of algorithms that can be applied at design time in order to maximally exploit scratch pad memories (SPMs). We show that both the energy consumption as well as the computed worst case execution time (WCET) can be reduced by up to to 80% and 48%, respectively, by establishing a strong link between the memory architecture and the compiler

Architecture extensions for efficient managament of scratch-pad Memory

Nowadays, many embedded processors include in their architecture on-chip static memories, so called scratch-pad memories (SPM). Compared to cache, these memories do not require complex control logic, thus resulting in increased efficiency both in silicon area and energy consumption. Last years, many papers have proposed algorithms to allocate memory segments in SPM in order to enhance its usage. However, very few care about the SPM architecture itself, to make it more controllable, more power efficient and faster. This paper proposes architecture extensions to automatically load code into the SPM whilst it is fetched for execution to reduce the SPM updating delays, which motivates a very dynamic use of the SPM. We test our proposal in a derivation of the Simplescalar simulator, with typical embedded benchmarks. The results show improvements, on average, of 30.6% in energy saving and 7.6% in performance compared to a system with cache. © 2011 Springer-Verlag.This research was sponsored by local Government “Generalitat Valenciana” under project GV07/ 2007/122.Busquets Mataix, JV.; Catalá, C.; Martí Campoy, A. (2011). Architecture extensions for efficient managament of scratch-pad Memory. En Integrated Circuit and System Design. Power and Timing Modeling, Optimization, and Simulation. Springer Verlag (Germany). (6951):43-52. https://doi.org/10.1007/978-3-642-24154-3_5S43526951Banakar, R., Steinke, S., Lee, B.-S., Balakrishnan, M., Marwedel, P.: Scratchpad memory: design alternative for cache on-chip memory in embedded systems. In: CODES 2002, pp. 73–78 (2002)Verma, M., Wehmeyer, L., Marwedel, P.: Cache-Aware Scratchpad Allocation Algorithm. In: DATE 2004, pp. 1264–1269 (2004)Verma, M., Marwedel, P.: Advanced memory optimization techniques for low-power embedded processors, pp. I-XII, 1–188. Springer, Heidelberg (2007)Nguyen, N., Dominguez, A., Barua, R.: Memory allocation for embedded systems with a compile-time-unknown scratch-pad size. In: CASES 2005, pp. 115–125 (2005)Egger, B., Kim, C., Jang, C., Nam, Y., Lee, J., Min, S.L.: A dynamic code placement technique for scratchpad memory using postpass optimization. In: CASES 2006, pp. 223–233 (2006)Egger, B., Lee, J., Shin, H.: Scratchpad memory management for portable systems with a memory management unit. In: EMSOFT 2006, pp. 321–330 (2006)Egger, B., Lee, J., Shin, H.: Dynamic scratchpad memory management for code in portable systems with an MMU. ACM Trans. Embedded Comput. Syst. 7(2) (2008)Cho, H., Egger, B., Lee, J., Shin, H.: Dynamic data scratchpad memory management for a memory subsystem with an MMU. In: LCTES 2007, pp. 195–206 (2007)Janapsatya, A., Parameswaran, S., Ignjatovic, A.: Hardware/software managed scratchpad memory for embedded system. In: ICCAD 2004, pp. 370–377 (2004)Balakrishnan, M., Marwedel, P., Wehmeyer, L., Grunwald, N., Banakar, R., Steinke, S.: Reducing Energy Consumption by Dynamic Copying of Instructions onto Onchip Memory. In: ISSS 2002, pp. 213–218 (2002)Poletti, F., Marchal, P., Atienza, D., Benini, L., Catthoor, F., Mendias, J.M.: An integrated hardware/software approach for run-time scratchpad management. In: DAC 2004, pp. 238–243 (2004)Li, L., Gao, L., Xue, J.: Memory Coloring: A Compiler Approach for Scratchpad Memory Management. In: IEEE PACT 2005, pp. 329–338 (2005)Lee, L.H., Moyer, B., Arends, J.: Instruction fetch energy reduction using loop caches for embedded applications with small tight loops. In: ISLPED 1999, pp. 267–269 (1999)Victorio, J.A., Torres Moren, E.F., Yúfera, V.V.: Vatios: Simulador de Procesador con Estimación de Potencia. XVIII Jornadas de Paralelismo, Zaragoza (2007)Burger, D., Austin, T.M.: The SimpleScalar Tool Set Version 2.0. Technical Report 1342, Computer Sciences Department. University of Wisconsin–Madison (May 1997)Brooks, D., Tiwari, V., Martonosi, M.: Wattch: a framework for architectural-level power analysis and optimizations. In: ISCA 2000, pp. 83–94 (2000)Tarjan, D., Thoziyoor, S., Jouppi, N.: CACTI 4.0, P. HPL-2006- 86 20060606The Mälardalen WCET research group. The Mälardalen WCET benchmarks homepage, http://www.mrtc.mdh.se/projects/wcet/benchmarks.htmlCho, D., Pasricha, S., Issenin, I., Dutt, N.D., Ahn, M., Paek, Y.: Adaptive Scratch Pad Memory Management for Dynamic Behavior of Multimedia Applications. IEEE Trans. on CAD of Integrated Circuits and Systems (TCAD) 28(4), 554–567 (2009

WCET-Aware Scratchpad Memory Management for Hard Real-Time Systems

abstract: Cyber-physical systems and hard real-time systems have strict timing constraints that specify deadlines until which tasks must finish their execution. Missing a deadline can cause unexpected outcome or endanger human lives in safety-critical applications, such as automotive or aeronautical systems. It is, therefore, of utmost importance to obtain and optimize a safe upper bound of each task’s execution time or the worst-case execution time (WCET), to guarantee the absence of any missed deadline. Unfortunately, conventional microarchitectural components, such as caches and branch predictors, are only optimized for average-case performance and often make WCET analysis complicated and pessimistic. Caches especially have a large impact on the worst-case performance due to expensive off- chip memory accesses involved in cache miss handling. In this regard, software-controlled scratchpad memories (SPMs) have become a promising alternative to caches. An SPM is a raw SRAM, controlled only by executing data movement instructions explicitly at runtime, and such explicit control facilitates static analyses to obtain safe and tight upper bounds of WCETs. SPM management techniques, used in compilers targeting an SPM-based processor, determine how to use a given SPM space by deciding where to insert data movement instructions and what operations to perform at those program locations. This dissertation presents several management techniques for program code and stack data, which aim to optimize the WCETs of a given program. The proposed code management techniques include optimal allocation algorithms and a polynomial-time heuristic for allocating functions to the SPM space, with or without the use of abstraction of SPM regions, and a heuristic for splitting functions into smaller partitions. The proposed stack data management technique, on the other hand, finds an optimal set of program locations to evict and restore stack frames to avoid stack overflows, when the call stack resides in a size-limited SPM. In the evaluation, the WCETs of various benchmarks including real-world automotive applications are statically calculated for SPMs and caches in several different memory configurations.Dissertation/ThesisDoctoral Dissertation Computer Science 201

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