Cross-Layer Pathfinding for Off-Chip Interconnects

Off-chip interconnects for integrated circuits (ICs) today induce a diverse design space, spanning many different applications that require transmission of data at various bandwidths, latencies and link lengths. Off-chip interconnect design solutions are also variously sensitive to system performance, power and cost metrics, while also having a strong impact on these metrics. The costs associated with off-chip interconnects include die area, package (PKG) and printed circuit board (PCB) area, technology and bill of materials (BOM). Choices made regarding off-chip interconnects are fundamental to product definition, architecture, design implementation and technology enablement. Given their cross-layer impact, it is imperative that a cross-layer approach be employed to architect and analyze off-chip interconnects up front, so that a top-down design flow can comprehend the cross-layer impacts and correctly assess the system performance, power and cost tradeoffs for off-chip interconnects. Chip architects are not exposed to all the tradeoffs at the physical and circuit implementation or technology layers, and often lack the tools to accurately assess off-chip interconnects. Furthermore, the collaterals needed for a detailed analysis are often lacking when the chip is architected; these include circuit design and layout, PKG and PCB layout, and physical floorplan and implementation. To address the need for a framework that enables architects to assess the system-level impact of off-chip interconnects, this thesis presents power-area-timing (PAT) models for off-chip interconnects, optimization and planning tools with the appropriate abstraction using these PAT models, and die/PKG/PCB co-design methods that help expose the off-chip interconnect cross-layer metrics to the die/PKG/PCB design flows. Together, these models, tools and methods enable cross-layer optimization that allows for a top-down definition and exploration of the design space and helps converge on the correct off-chip interconnect implementation and technology choice. The tools presented cover off-chip memory interfaces for mobile and server products, silicon photonic interfaces, 2.5D silicon interposers and 3D through-silicon vias (TSVs). The goal of the cross-layer framework is to assess the key metrics of the interconnect (such as timing, latency, active/idle/sleep power, and area/cost) at an appropriate level of abstraction by being able to do this across layers of the design flow. In additional to signal interconnect, this thesis also explores the need for such cross-layer pathfinding for power distribution networks (PDN), where the system-on-chip (SoC) floorplan and pinmap must be optimized before the collateral layouts for PDN analysis are ready. Altogether, the developed cross-layer pathfinding methodology for off-chip interconnects enables more rapid and thorough exploration of a vast design space of off-chip parallel and serial links, inter-die and inter-chiplet links and silicon photonics. Such exploration will pave the way for off-chip interconnect technology enablement that is optimized for system needs. The basis of the framework can be extended to cover other interconnect technology as well, since it fundamentally relates to system-level metrics that are common to all off-chip interconnects

ARMAN: A Reconfigurable Monolithic 3D Accelerator Architecture for Convolutional Neural Networks

The Convolutional Neural Network (CNN) has emerged as a powerful and versatile tool for artificial intelligence (AI) applications. Conventional computing architectures face challenges in meeting the demanding processing requirements of compute-intensive CNN applications, as they suffer from limited throughput and low utilization. To this end, specialized accelerators have been developed to speed up CNN computations. However, as we demonstrate in this paper via extensive design space exploration, different neural network models have different characteristics, which calls for different accelerator architectures and configurations to match their computing demand. We show that a one-size-fits-all fixed architecture does not guarantee optimal power/energy/performance trade-off. To overcome this challenge, this paper proposes ARMAN, a novel reconfigurable systolic-array-based accelerator architecture based on Monolithic 3D (M3D) technology for CNN inference. The proposed accelerator offers the flexibility to reconfigure among different scale-up or scale-out arrangements depending on the neural network structure, providing the optimal trade-off across power, energy, and performance for various neural network models. We demonstrate the effectiveness of our approach through evaluations of multiple benchmarks. The results demonstrate that the proposed accelerator exhibits up to 2x, 2.24x, 1.48x, and 2x improvements in terms of execution cycles, power, energy, and EDP respectively, over the non-configurable architecture

Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators

The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters. Various methods of memory organisation targeting energy-efficient digital accelerators have been investigated in the past, however, they do not completely encapsulate the energy costs at a system level. To address this shortcoming and to account for various overheads, we synthesize the controller and memory for different encoding schemes and extract the energy costs from these synthesized blocks. Additionally, we introduce functional encoding for structured connectivity such as the connectivity in convolutional layers. Functional encoding offers a 58% reduction in the energy to implement a backward pass and weight update in such layers compared to existing index-based solutions. We show that for a 2 layer spiking neural network trained to retain a spatio-temporal pattern, bitmap (PB-BMP) based organization can encode the sparser networks more efficiently. This form of encoding delivers a 1.37x improvement in energy efficiency coming at the cost of a 4% degradation in network retention accuracy as measured by the van Rossum distance.Comment: submitted to ISCAS202

Improvement Energy Efficiency for a Hybrid Multibank Memory in Energy Critical Applications

High performance, low power multiprocessor/multibank memory system requires a compiler that provides efficient data partitioning and mapping procedures. This paper introduced two compiler techniques for the data mapping to multibank memory, since data mapping is still an open problem and needs a better solution. The multibank memory can be consisted of volatile and non-volatile memory components to support ultra-low powered wearable devices. This hybrid memory system including volatile and non-volatile memory components yields higher complexity to map data onto it. To efficiently solve this mapping problem, we formulate it to a simple decision problem. Based on the problem definition, we proposed two efficient algorithms to determine the placement of data to the multibank memory. The proposed techniques consider the characteristic of the non-volatile memory that its write operation consumes more energy than the same operation of a volatile memory even though it provides ultra-low operation power and nearly zero leakage current. The proposed technique solves this negative effect of non-volatile memory by using efficient data placement technique and hybrid memory architecture. In experimental section, the result shows that the proposed techniques improve energy saving up to 59.5% for the hybrid multibank memory architecture

Runtime-assisted optimizations in the on-chip memory hierarchy

Following Moore's Law, the number of transistors on chip has been increasing exponentially, which has led to the increasing complexity of modern processors. As a result, the efficient programming of such systems has become more difficult. Many programming models have been developed to answer this issue. Of particular interest are task-based programming models that employ simple annotations to define parallel work in an application. The information available at the level of the runtime systems associated with these programming models offers great potential for improving hardware design. Moreover, due to technological limitations, Moore's Law is predicted to eventually come to an end, so novel paradigms are necessary to maintain the current performance improvement trends. The main goal of this thesis is to exploit the knowledge about a parallel application available at the runtime system level to improve the design of the on-chip memory hierarchy. The coupling of the runtime system and the microprocessor enables a better hardware design without hurting the programmability. The first contribution is a set of insertion policies for shared last-level caches that exploit information about tasks and task data dependencies. The intuition behind this proposal revolves around the observation that parallel threads exhibit different memory access patterns. Even within the same thread, accesses to different variables often follow distinct patterns. The proposed policies insert cache lines into different logical positions depending on the dependency type and task type to which the corresponding memory request belongs. The second proposal optimizes the execution of reductions, defined as a programming pattern that combines input data to form the resulting reduction variable. This is achieved with a runtime-assisted technique for performing reductions in the processor's cache hierarchy. The proposal's goal is to be a universally applicable solution regardless of the reduction variable type, size and access pattern. On the software level, the programming model is extended to let a programmer specify the reduction variables for tasks, as well as the desired cache level where a certain reduction will be performed. The source-to-source compiler and the runtime system are extended to translate and forward this information to the underlying hardware. On the hardware level, private and shared caches are equipped with functional units and the accompanying logic to perform reductions at the cache level. This design avoids unnecessary data movements to the core and back as the data is operated at the place where it resides. The third contribution is a runtime-assisted prioritization scheme for memory requests inside the on-chip memory hierarchy. The proposal is based on the notion of a critical path in the context of parallel codes and a known fact that accelerating critical tasks reduces the execution time of the whole application. In the context of this work, task criticality is observed at a level of a task type as it enables simple annotation by the programmer. The acceleration of critical tasks is achieved by the prioritization of corresponding memory requests in the microprocessor.Siguiendo la ley de Moore, el número de transistores en los chips ha crecido exponencialmente, lo que ha comportado una mayor complejidad en los procesadores modernos y, como resultado, de la dificultad de la programación eficiente de estos sistemas. Se han desarrollado muchos modelos de programación para resolver este problema; un ejemplo particular son los modelos de programación basados en tareas, que emplean anotaciones sencillas para definir los Trabajos paralelos de una aplicación. La información de que disponen los sistemas en tiempo de ejecución (runtime systems) asociada con estos modelos de programación ofrece un enorme potencial para la mejora del diseño del hardware. Por otro lado, las limitaciones tecnológicas hacen que la ley de Moore pueda dejar de cumplirse próximamente, por lo que se necesitan paradigmas nuevos para mantener las tendencias actuales de mejora de rendimiento. El objetivo principal de esta tesis es aprovechar el conocimiento de las aplicaciones paral·leles de que dispone el runtime system para mejorar el diseño de la jerarquía de memoria del chip. El acoplamiento del runtime system junto con el microprocesador permite realizar mejores diseños hardware sin afectar Negativamente en la programabilidad de dichos sistemas. La primera contribución de esta tesis consiste en un conjunto de políticas de inserción para las memorias caché compartidas de último nivel que aprovecha la información de las tareas y las dependencias de datos entre estas. La intuición tras esta propuesta se basa en la observación de que los hilos de ejecución paralelos muestran distintos patrones de acceso a memoria e, incluso dentro del mismo hilo, los accesos a diferentes variables a menudo siguen patrones distintos. Las políticas que se proponen insertan líneas de caché en posiciones lógicas diferentes en función de los tipos de dependencia y tarea a los que corresponde la petición de memoria. La segunda propuesta optimiza la ejecución de las reducciones, que se definen como un patrón de programación que combina datos de entrada para conseguir la variable de reducción como resultado. Esto se consigue mediante una técnica asistida por el runtime system para la realización de reducciones en la jerarquía de la caché del procesador, con el objetivo de ser una solución aplicable de forma universal sin depender del tipo de la variable de la reducción, su tamaño o el patrón de acceso. A nivel de software, el modelo de programación se extiende para que el programador especifique las variables de reducción de las tareas, así como el nivel de caché escogido para que se realice una determinada reducción. El compilador fuente a Fuente (compilador source-to-source) y el runtime ssytem se modifican para que traduzcan y pasen esta información al hardware subyacente, evitando así movimientos de datos innecesarios hacia y desde el núcleo del procesador, al realizarse la operación donde se encuentran los datos de la misma. La tercera contribución proporciona un esquema de priorización asistido por el runtime system para peticiones de memoria dentro de la jerarquía de memoria del chip. La propuesta se basa en la noción de camino crítico en el contexto de los códigos paralelos y en el hecho conocido de que acelerar tareas críticas reduce el tiempo de ejecución de la aplicación completa. En el contexto de este trabajo, la criticidad de las tareas se considera a nivel del tipo de tarea ya que permite que el programador las indique mediante anotaciones sencillas. La aceleración de las tareas críticas se consigue priorizando las correspondientes peticiones de memoria en el microprocesador.Seguint la llei de Moore, el nombre de transistors que contenen els xips ha patit un creixement exponencial, fet que ha provocat un augment de la complexitat dels processadors moderns i, per tant, de la dificultat de la programació eficient d’aquests sistemes. Per intentar solucionar-ho, s’han desenvolupat diversos models de programació; un exemple particular en són els models basats en tasques, que fan servir anotacions senzilles per definir treballs paral·lels dins d’una aplicació. La informació que hi ha al nivell dels sistemes en temps d’execució (runtime systems) associada amb aquests models de programació ofereix un gran potencial a l’hora de millorar el disseny del maquinari. D’altra banda, les limitacions tecnològiques fan que la llei de Moore pugui deixar de complir-se properament, per la qual cosa calen nous paradigmes per mantenir les tendències actuals en la millora de rendiment. L’objectiu principal d’aquesta tesi és aprofitar els coneixements que el runtime System té d’una aplicació paral·lela per millorar el disseny de la jerarquia de memòria dins el xip. L’acoblament del runtime system i el microprocessador permet millorar el disseny del maquinari sense malmetre la programabilitat d’aquests sistemes. La primera contribució d’aquesta tesi consisteix en un conjunt de polítiques d’inserció a les memòries cau (cache memories) compartides d’últim nivell que aprofita informació sobre tasques i les dependències de dades entre aquestes. La intuïció que hi ha al darrere d’aquesta proposta es basa en el fet que els fils d’execució paral·lels mostren diferents patrons d’accés a la memòria; fins i tot dins el mateix fil, els accessos a variables diferents sovint segueixen patrons diferents. Les polítiques que s’hi proposen insereixen línies de la memòria cau a diferents ubicacions lògiques en funció dels tipus de dependència i de tasca als quals correspon la petició de memòria. La segona proposta optimitza l’execució de les reduccions, que es defineixen com un patró de programació que combina dades d’entrada per aconseguir la variable de reducció com a resultat. Això s’aconsegueix mitjançant una tècnica assistida pel runtime system per dur a terme reduccions en la jerarquia de la memòria cau del processador, amb l’objectiu que la proposta sigui aplicable de manera universal, sense dependre del tipus de la variable a la qual es realitza la reducció, la seva mida o el patró d’accés. A nivell de programari, es realitza una extensió del model de programació per facilitar que el programador especifiqui les variables de les reduccions que usaran les tasques, així com el nivell de memòria cau desitjat on s’hauria de realitzar una certa reducció. El compilador font a font (compilador source-to-source) i el runtime system s’amplien per traduir i passar aquesta informació al maquinari subjacent. A nivell de maquinari, les memòries cau privades i compartides s’equipen amb unitats funcionals i la lògica corresponent per poder dur a terme les reduccions a la pròpia memòria cau, evitant així moviments de dades innecessaris entre el nucli del processador i la jerarquia de memòria. La tercera contribució proporciona un esquema de priorització assistit pel runtime System per peticions de memòria dins de la jerarquia de memòria del xip. La proposta es basa en la noció de camí crític en el context dels codis paral·lels i en el fet conegut que l’acceleració de les tasques que formen part del camí crític redueix el temps d’execució de l’aplicació sencera. En el context d’aquest treball, la criticitat de les tasques s’observa al nivell del seu tipus ja que permet que el programador les indiqui mitjançant anotacions senzilles. L’acceleració de les tasques crítiques s’aconsegueix prioritzant les corresponents peticions de memòria dins el microprocessador

Doctor of Philosophy

dissertationIn recent years, a number of trends have started to emerge, both in microprocessor and application characteristics. As per Moore's law, the number of cores on chip will keep doubling every 18-24 months. International Technology Roadmap for Semiconductors (ITRS) reports that wires will continue to scale poorly, exacerbating the cost of on-chip communication. Cores will have to navigate an on-chip network to access data that may be scattered across many cache banks. The number of pins on the package, and hence available off-chip bandwidth, will at best increase at sublinear rate and at worst, stagnate. A number of disruptive memory technologies, e.g., phase change memory (PCM) have begun to emerge and will be integrated into the memory hierarchy sooner than later, leading to non-uniform memory access (NUMA) hierarchies. This will make the cost of accessing main memory even higher. In previous years, most of the focus has been on deciding the memory hierarchy level where data must be placed (L1 or L2 caches, main memory, disk, etc.). However, in modern and future generations, each level is getting bigger and its design is being subjected to a number of constraints (wire delays, power budget, etc.). It is becoming very important to make an intelligent decision about where data must be placed within a level. For example, in a large non-uniform access cache (NUCA), we must figure out the optimal bank. Similarly, in a multi-dual inline memory module (DIMM) non uniform memory access (NUMA) main memory, we must figure out the DIMM that is the optimal home for every data page. Studies have indicated that heterogeneous main memory hierarchies that incorporate multiple memory technologies are on the horizon. We must develop solutions for data management that take heterogeneity into account. For these memory organizations, we must again identify the appropriate home for data. In this dissertation, we attempt to verify the following thesis statement: "Can low-complexity hardware and OS mechanisms manage data placement within each memory hierarchy level to optimize metrics such as performance and/or throughput?" In this dissertation we argue for a hardware-software codesign approach to tackle the above mentioned problems at different levels of the memory hierarchy. The proposed methods utilize techniques like page coloring and shadow addresses and are able to handle a large number of problems ranging from managing wire-delays in large, shared NUCA caches to distributing shared capacity among different cores. We then examine data-placement issues in NUMA main memory for a many-core processor with a moderate number of on-chip memory controllers. Using codesign approaches, we achieve efficient data placement by modifying the operating system's (OS) page allocation algorithm for a wide variety of main memory architectures

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