A Holistic Solution for Reliability of 3D Parallel Systems

As device scaling slows down, emerging technologies such as 3D integration and carbon nanotube field-effect transistors are among the most promising solutions to increase device density and performance. These emerging technologies offer shorter interconnects, higher performance, and lower power. However, higher levels of operating temperatures and current densities project significantly higher failure rates. Moreover, due to the infancy of the manufacturing process, high variation, and defect densities, chip designers are not encouraged to consider these emerging technologies as a stand-alone replacement for Silicon-based transistors. The goal of this dissertation is to introduce new architectural and circuit techniques that can work around high-fault rates in the emerging 3D technologies, improving performance and reliability comparable to Silicon. We propose a new holistic approach to the reliability problem that addresses the necessary aspects of an effective solution such as detection, diagnosis, repair, and prevention synergically for a practical solution. By leveraging 3D fabric layouts, it proposes the underlying architecture to efficiently repair the system in the presence of faults. This thesis presents a fault detection scheme by re-executing instructions on idle identical units that distinguishes between transient and permanent faults while localizing it to the granularity of a pipeline stage. Furthermore, with the use of a dynamic and adaptive reconfiguration policy based on activity factors and temperature variation, we propose a framework that delivers a significant improvement in lifetime management to prevent faults due to aging. Finally, a design framework that can be used for large-scale chip production while mitigating yield and variation failures to bring up Carbon Nano Tube-based technology is presented. The proposed framework is capable of efficiently supporting high-variation technologies by providing protection against manufacturing defects at different granularities: module and pipeline-stage levels.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168118/1/javadb_1.pd

METHODOLOGY AND ANALYSIS FOR EFFICIENT CUSTOM ARCHITECTURE DESIGN USING MACHINE LEARNING

Machine learning algorithms especially Deep Neural Networks (DNNs) have revolutionized the arena of computing in the last decade. DNNs along the with the computational advancements also bring an unprecedented appetite for compute and parallel processing. Computer architects have risen to challenge by creating novel custom architectures called accelerators. However, given the ongoing rapid advancements in algorithmic development accelerators architects are playing catch- up to churn out optimized designs each time new algorithmic changes are published. It is also worth noting that the accelerator design cycle is expensive. It requires multiple iteration of design space optimization and expert knowledge of both digital design as well as domain knowledge of the workload itself. It is therefore imperative to build scalable and flexible architectures which are adaptive to work well for a variety of workloads. Moreover, it is also important to develop relevant tools and design methodologies which lower the overheads incurred at design time such that subsequent design iterations are fast and sustainable. This thesis takes a three-pronged approach to address these problems and push the frontiers for DNN accelerator design process. First, the thesis presents the description of a now popular cycle accurate DNN accelerator simulator. This simulator is built with the goal of obtaining detailed metrics as fast as possible. A detailed analytical model is also presented in this thesis which enables the designer to understand the interactions of the workload and architecture parameters. The information from the model can be directly used to prune the design search space to achieve faster convergence. Second, the thesis details a couple of flexible yet scalable DNN accelerator architectures. Finally, this thesis describes the use of machine learning to capture the design space of DNN accelerators and train a model to predict optimum configurations when queried with workload parameters and design constraints. The novelty of this piece of work is that it systematically lays out the formulation of traditional design optimization into a machine learning problem and describes the quality and components of a model which works well across various architecture design tasks.Ph.D

Energy efficient core designs for upcoming process technologies

Energy efficiency has been a first order constraint in the design of micro processors for the last decade. As Moore's law sunsets, new technologies are being actively explored to extend the march in increasing the computational power and efficiency. It is essential for computer architects to understand the opportunities and challenges in utilizing the upcoming process technology trends in order to design the most efficient processors. In this work, we consider three process technology trends and propose core designs that are best suited for each of the technologies. The process technologies are expected to be viable over a span of timelines. We first consider the most popular method currently available to improve the energy efficiency, i.e. by lowering the operating voltage. We make key observations regarding the limiting factors in scaling down the operating voltage for general purpose high performance processors. Later, we propose our novel core design, ScalCore, one that can work in high performance mode at nominal Vdd, and in a very energy-efficient mode at low Vdd. The resulting core design can operate at much lower voltages providing higher parallel performance while consuming lower energy. While lowering Vdd improves the energy efficiency, CMOS devices are fundamentally limited in their low voltage operation. Therefore, we next consider an upcoming device technology -- Tunneling Field-Effect Transistors (TFETs), that is expected to supplement CMOS device technology in the near future. TFETs can attain much higher energy efficiency than CMOS at low voltages. However, their performance saturates at high voltages and, therefore, cannot entirely replace CMOS when high performance is needed. Ideally, we desire a core that is as energy-efficient as TFET and provides as much performance as CMOS. To reach this goal, we characterize the TFET device behavior for core design and judiciously integrate TFET units, CMOS units in a single core. The resulting core, called HetCore, can provide very high energy efficiency while limiting the slowdown when compared to a CMOS core. Finally, we analyze Monolithic 3D (M3D) integration technology that is widely considered to be the only way to integrate more transistors on a chip. We present the first analysis of the architectural implications of using M3D for core design and show how to partition the core across different layers. We also address one of the key challenges in realizing the technology, namely, the top layer performance degradation. We propose a critical path based partitioning for logic stages and asymmetric bit/port partitioning for storage stages. The result is a core that performs nearly as well as a core without any top layer slowdown. When compared to a 2D baseline design, an M3D core not only provides much higher performance, it also reduces the energy consumption at the same time. In summary, this thesis addresses one of the fundamental challenges in computer architecture -- overcoming the fact that CMOS is not scaling anymore. As we increase the computing power on a single chip, our ability to power the entire chip keeps decreasing. This thesis proposes three solutions aimed at solving this problem over different timelines. Across all our solutions, we improve energy efficiency without compromising the performance of the core. As a result, we are able to operate twice as many cores with in the same power budget as regular cores, significantly alleviating the problem of dark silicon

Building Efficient and Reliable Emerging Technology Systems

The semiconductor industry has been reaping the benefits of Moore’s law powered by Dennard’s voltage scaling for the past fifty years. However, with the end of Dennard scaling, silicon chip manufacturers are facing a widespread plateau in performance improvements. While the architecture community has focused its effort on exploring parallelism, such as with multi-core, many-core and accelerator-based systems, chip manufacturers have been forced to explore beyond-Moore technologies to improve performance while maintaining power density. Examples of such technologies include monolithic 3D integration, carbon nanotube transistors, tunneling-based transistors, spintronics and quantum computing. However, the infancy of the manufacturing process of these new technologies impedes their usage in commercial products. The goal of this dissertation is to combine both architectural and device-level efforts to provide solutions across the computing stack that can overcome the reliability concerns of emerging technologies. This allows for beyond-Moore systems to compete with highly optimized silicon-based processors, thus, enabling faster commercialization of such systems. This dissertation proposes the following key steps: (i) Multifaceted understanding and modeling of variation and yield issues that occur in emerging technologies, such as carbon nanotube transistors (CNFETs). (ii) Design of systems using suitable logic families such as pass transistor logic that provide high performance. (iii) Design of a multi-granular fault-tolerant reconfigurable architecture that enhances yield and performance. (iv) Design of a multi-technology, multi-accelerator heterogeneous system (v) Development of real-time constrained efficient workload scheduling mechanism for heterogeneous systems. This dissertation first presents the use of pass transistor logic family as an alternate to the CMOS logic family for CNFETs to improve performance. It explores various architectural design choices for CNFETs using pass transistor logic (PTL) to create an energy-efficient RISC-V processor. Our results show that while a CNFET RISC-V processor using CMOS logic achieves a 2.9x energy-delay product (EDP) improvement over a silicon design, using PTL along the critical path components of the processor can boost EDP improvement by 5x as well as reduce area by 17% over 16 nm silicon CMOS. This document further builds on providing fault-tolerant and yield enhancing solutions for emerging 3D integration compatible technologies in the context of CNFETs. The proposed framework can efficiently support high-variation technologies by providing protection against manufacturing defects at multiple granularities: module and pipeline-stage levels. Based on the variation observed in a synthesized design, a reliable CNFET-based 3D multi-granular reconfigurable architecture, 3DTUBE, is presented to overcome the manufacturing difficulties. For 0.4-0.7 V, 3DTUBE provides up to 6.0x higher throughput and 3.1x lower EDP compared to a silicon-based multi-core design evaluated at 1 part per billion transistor failure rate, which is 10,000x lower in comparison to CNFET’s failure rate. This dissertation then ventures into building multi-accelerator heterogeneous systems and real-time schedulers that cater to the requirements of the applications while taking advantage of the underlying heterogeneous system. We introduce optimizations like task pruning, hierarchical hetero-ranking and rank update built upon two scheduler policies (MS-static and MS-dynamic), that result in a performance improvement of 3.5x (average) for real-world autonomous vehicle applications, when compared against state-of-the-art schedulers. Adopting insights from the above work, this thesis presents a multi-accelerator, multi-technology heterogeneous system powered by a multi-constrained scheduler that optimizes for varying task requirements to achieve up to 6.1x better energy over a baseline silicon-based system.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169699/1/aporvaa_1.pd

Single chip solution for stabilization control & monocular visual servoing of small-scale quadrotor helicopter

This thesis documents the research undertaken to develop a high-performing design of a small-scale quadrotor (four-rotor) helicopter capable of delivering the speed and robustness required for agile motion while also featuring an autonomous visual servoing capability within the size, weight, and power (SWaP) constraint package. The state of the art research was reviewed, and the areas in the existing design methodologies that can potentially be improved were identified, which included development of a comprehensive dynamics model of quadrotor, design and construction of a performance optimized prototype vehicle, high-performance actuator design, design of a robust attitude stabilization controller, and a single chip solution for autonomous vision based position control. The gaps in the current art of designing each component were addressed individually. The outcomes of the corresponding development activities include a high-fidelity dynamics and control model of the vehicle. The model was developed using multi-body bond graph modeling approach to incorporate the dynamic interactions between the frame body and propulsion system. Using an algorithmic size, payload capacity, and flight endurance optimization approach, a quadrotor prototype was designed and constructed. In order to conform to the optimized geometric and performance parameters, the frame of the prototype was constructed using printed circuit board (PCB) technology and processing power was integrated using a single chip field programmable gate array (FPGA) technology. Furthermore, to actuate the quadrotor at a high update rate while also improving the power efficiency of the actuation system, a ground up FPGA based brushless direct current (BLDC) motor driver was designed using a low-loss commutation scheme and hall effect sensors. A proportional-integral-derivative (PID) technology based closed loop motor speed controller was also implemented in the same FPGA hardware for precise speed control of the motors. In addition, a novel control law was formulated for robust attitude stabilization by adopting a cascaded architecture of active disturbance rejection control (ADRC) technology and PID control technology. Using the same single FPGA chip to drive an on-board downward looking camera, a monocular visual servoing solution was developed to integrate an autonomous position control feature with the quadrotor. Accordingly, a numerically simple relative position estimation technique was implemented in FPGA hardware that relies on a passive landmark/target for 3-D position estimation. The functionality and effectiveness of the synthesized design were evaluated by performance benchmarking experiments conducted on each individual component as well as on the complete system constructed from these components. It was observed that the proposed small-scale quadrotor, even though just 43 cm in diameter, can lift 434 gm of payload while operating for 18 min. Among the ground up designed components, the FPGA based motor driver demonstrated a maximum of 4% improvement in the power consumption and at the same time can handle a command update at a rate of 16 kHz. The cascaded attitude stabilization controller can asymptotically stabilize the vehicle within 426 ms of the command update. Robust control performance under stochastic wind gusts is also observed from the stabilization controller. Finally, the single chip FPGA based monocular visual servoing solution can estimate pose information at the camera rate of 37 fps and accordingly the quadrotor can autonomously climb/descend and/or hover over a passive target

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

TERPS: The Embedded Reliable Processing System

Electromagnetic Interference (EMI) can have an adverse effect on commercial electronics. As feature sizes of integrated circuits become smaller, their susceptibility to EMI increases. In light of this, integrated circuits will face substantial problems in the future either from electromagnetic disturbances or intentionally generated EMI from a malicious source. The Embedded Reliable Processing System (TERPS) is a fault tolerant system architecture which can significantly reduce the threat of EMI in computer systems. TERPS employs a checkpoint and rollback recovery mechanism tied with a multi-phase commit protocol and 3D IC technology. This enables it to recover from substantial EMI without having to shutdown or reboot. In the face of such EMI, only a loss in performance dictated by the strength and duration of the interference and the frequency of checkpointing will be seen. Various conditions in which chips can fail under the influence of EMI are described. The checkpoint and rollback recovery mechanism and the resulting TERPS architecture is stipulated. A thorough evaluation of the design correctness is provided. The technique is implemented in Verilog HDL using a 16-bit, 5-stage pipelined processor to show proof of concept. The performance overhead is calculated for different checkpointing intervals and is shown to be very reasonable (5-6% for checkpointing every 128 CPU cycles)

A Modern Primer on Processing in Memory

Modern computing systems are overwhelmingly designed to move data to computation. This design choice goes directly against at least three key trends in computing that cause performance, scalability and energy bottlenecks: (1) data access is a key bottleneck as many important applications are increasingly data-intensive, and memory bandwidth and energy do not scale well, (2) energy consumption is a key limiter in almost all computing platforms, especially server and mobile systems, (3) data movement, especially off-chip to on-chip, is very expensive in terms of bandwidth, energy and latency, much more so than computation. These trends are especially severely-felt in the data-intensive server and energy-constrained mobile systems of today. At the same time, conventional memory technology is facing many technology scaling challenges in terms of reliability, energy, and performance. As a result, memory system architects are open to organizing memory in different ways and making it more intelligent, at the expense of higher cost. The emergence of 3D-stacked memory plus logic, the adoption of error correcting codes inside the latest DRAM chips, proliferation of different main memory standards and chips, specialized for different purposes (e.g., graphics, low-power, high bandwidth, low latency), and the necessity of designing new solutions to serious reliability and security issues, such as the RowHammer phenomenon, are an evidence of this trend. This chapter discusses recent research that aims to practically enable computation close to data, an approach we call processing-in-memory (PIM). PIM places computation mechanisms in or near where the data is stored (i.e., inside the memory chips, in the logic layer of 3D-stacked memory, or in the memory controllers), so that data movement between the computation units and memory is reduced or eliminated.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0398