Spintronics-based Reconfigurable Ising Model Architecture

Published in the International Symposium On Quality Electronic Design (ISQED), March 2020The Ising model has been explored as a framework for modeling NP-hard problems, with several diverse systems proposed to solve it. The Magnetic Tunnel Junction (MTJ)-based Magnetic RAM is capable of replacing CMOS in memory chips. In this paper, we propose the use of MTJs for representing the units of an Ising model and leveraging its intrinsic physics for finding the ground state of the system through annealing. We design the structure of a basic MTJ-based Ising cell capable of performing the functions essential to an Ising solver. A technique to use the basic Ising cell for scaling to large problems is described. We then go on to propose Ising-FPGA, a parallel and reconfigurable architecture that can be used to map a large class of NP-hard problems, and show how a standard Place and Route tool can be utilized to program the Ising-FPGA. The effects of this hardware platform on our proposed design are characterized and methods to overcome these effects are prescribed. We discuss how two representative NP-hard problems can be mapped to the Ising model. Simulation results show the effectiveness of MTJs as Ising units by producing solutions close/comparable to the optimum, and demonstrate that our design methodology holds the capability to account for the effects of the hardware.This work was supported by the National Science Foundation(NSF) under Grant 164242

Fault tolerance issues in nanoelectronics

The astonishing success story of microelectronics cannot go on indefinitely. In fact, once devices reach the few-atom scale (nanoelectronics), transient quantum effects are expected to impair their behaviour. Fault tolerant techniques will then be required. The aim of this thesis is to investigate the problem of transient errors in nanoelectronic devices. Transient error rates for a selection of nanoelectronic gates, based upon quantum cellular automata and single electron devices, in which the electrostatic interaction between electrons is used to create Boolean circuits, are estimated. On the bases of such results, various fault tolerant solutions are proposed, for both logic and memory nanochips. As for logic chips, traditional techniques are found to be unsuitable. A new technique, in which the voting approach of triple modular redundancy (TMR) is extended by cascading TMR units composed of nanogate clusters, is proposed and generalised to other voting approaches. For memory chips, an error correcting code approach is found to be suitable. Various codes are considered and a lookup table approach is proposed for encoding and decoding. We are then able to give estimations for the redundancy level to be provided on nanochips, so as to make their mean time between failures acceptable. It is found that, for logic chips, space redundancies up to a few tens are required, if mean times between failures have to be of the order of a few years. Space redundancy can also be traded for time redundancy. As for memory chips, mean times between failures of the order of a few years are found to imply both space and time redundancies of the order of ten

Towards Energy-Efficient and Reliable Computing: From Highly-Scaled CMOS Devices to Resistive Memories

The continuous increase in transistor density based on Moore\u27s Law has led us to highly scaled Complementary Metal-Oxide Semiconductor (CMOS) technologies. These transistor-based process technologies offer improved density as well as a reduction in nominal supply voltage. An analysis regarding different aspects of 45nm and 15nm technologies, such as power consumption and cell area to compare these two technologies is proposed on an IEEE 754 Single Precision Floating-Point Unit implementation. Based on the results, using the 15nm technology offers 4-times less energy and 3-fold smaller footprint. New challenges also arise, such as relative proportion of leakage power in standby mode that can be addressed by post-CMOS technologies. Spin-Transfer Torque Random Access Memory (STT-MRAM) has been explored as a post-CMOS technology for embedded and data storage applications seeking non-volatility, near-zero standby energy, and high density. Towards attaining these objectives for practical implementations, various techniques to mitigate the specific reliability challenges associated with STT-MRAM elements are surveyed, classified, and assessed herein. Cost and suitability metrics assessed include the area of nanomagmetic and CMOS components per bit, access time and complexity, Sense Margin (SM), and energy or power consumption costs versus resiliency benefits. In an attempt to further improve the Process Variation (PV) immunity of the Sense Amplifiers (SAs), a new SA has been introduced called Adaptive Sense Amplifier (ASA). ASA can benefit from low Bit Error Rate (BER) and low Energy Delay Product (EDP) by combining the properties of two of the commonly used SAs, Pre-Charge Sense Amplifier (PCSA) and Separated Pre-Charge Sense Amplifier (SPCSA). ASA can operate in either PCSA or SPCSA mode based on the requirements of the circuit such as energy efficiency or reliability. Then, ASA is utilized to propose a novel approach to actually leverage the PV in Non-Volatile Memory (NVM) arrays using Self-Organized Sub-bank (SOS) design. SOS engages the preferred SA alternative based on the intrinsic as-built behavior of the resistive sensing timing margin to reduce the latency and power consumption while maintaining acceptable access time

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

Spintronics-based Architectures for non-von Neumann Computing

The scaling of transistor technology in the last few decades has significantly impacted our lives. It has given birth to different kinds of computational workloads which are becoming increasingly relevant. Some of the most prominent examples are Machine Learning based tasks such as image classification and pattern recognition which use Deep Neural Networks that are highly computation and memory-intensive. The traditional and general-purpose architectures that we use today typically exhibit high energy and latency on such computations. This, and the apparent end of Moore's law of scaling, has got researchers into looking for devices beyond CMOS and for computational paradigms that are non-conventional. In this dissertation, we focus on a spintronic device, the Magnetic Tunnel Junction (MTJ), which has demonstrated potential as cache and embedded memory. We look into how the MTJ can be used beyond memory and deployed in various non-conventional and non-von Neumann architectures for accelerating computations or making them energy efficient. First, we investigate into Stochastic Computing (SC) and show how MTJs can be used to build energy-efficient Neural Network (NN) hardware in this domain. SC is primarily bit-serial computing which requires simple logic gates for arithmetic operations. We explore the use of MTJs as Stochastic Number Generators (SNG) by exploiting their probabilistic switching characteristics and propose an energy-efficient MTJ-SNG. It is deployed as part of an NN hardware implemented in the SC domain. Its characteristics allow for achieving further energy efficiency through NN weight approximation, towards which we develop an optimization problem. Next, we turn our attention to analog computing and propose a method for training of analog Neural Network hardware. We consider a resistive MTJ crossbar architecture for representing an NN layer since it is capable of in-memory computing and performs matrix-vector multiplications with O(1) time complexity. We propose the on-chip training of the NN crossbar since, first, it can leverage the parallelism in the crossbar to perform weight update, second, it allows to take into account the device variations, and third, it enables avoiding large sneak currents in transistor-less crossbars which can cause undesired weight changes. Lastly, we propose an MTJ-based non-von Neumann hardware platform for solving combinatorial optimization problems since they are NP-hard. We adopt the Ising model for encoding such problems and solving them with simulated annealing. We let MTJs represent Ising units, design a scalable circuit capable of performing Ising computations and develop a reconfigurable architecture to which any NP-hard problem can be mapped. We also suggest methods to take into account the non-idealities present in the proposed hardware

Performance analysis of fault-tolerant nanoelectronic memories

Performance growth in microelectronics, as described by Moore’s law, is steadily approaching its limits. Nanoscale technologies are increasingly being explored as a practical solution to sustaining and possibly surpassing current performance trends of microelectronics. This work presents an in-depth analysis of the impact on performance, of incorporating reliability schemes into the architecture of a crossbar molecular switch nanomemory and demultiplexer. Nanoelectronics are currently in their early stages, and so fabrication and design methodologies are still in the process of being studied and developed. The building blocks of nanotechnology are fabricated using bottom-up processes, which leave them highly susceptible to defects. Hence, it is very important that defect and fault-tolerant schemes be incorporated into the design of nanotechnology related devices. In this dissertation, we focus on the study of a novel and promising class of computer chip memories called crossbar molecular switch memories and their demultiplexer addressing units. A major part of this work was the design of a defect and fault tolerance scheme we called the Multi-Switch Junction (MSJ) scheme. The MSJ scheme takes advantage of the regular array geometry of the crossbar nanomemory to create multiple switches in the fabric of the crossbar nanomemory for the storage of a single bit. Implementing defect and fault tolerant schemes come at a performance cost to the crossbar nanomemory; the challenge becomes achieving a balance between device reliability and performance. We have studied the reliability induced performance penalties as they relate to the time (delay) it takes to access a bit, and the amount of power dissipated by the process. Also, MSJ was compared to the banking and error correction coding fault tolerant schemes. Studies were also conducted to ascertain the potential benefits of integrating our MSJ scheme with the banking scheme. Trade-off analysis between access time delay, power dissipation and reliability is outlined and presented in this work. Results show the MSJ scheme increases the reliability of the crossbar nanomemory and demultiplexer. Simulation results also indicated that MSJ works very well for smaller nanomemory array sizes, with reliabilities of 100% for molecular switch failure rates in the 10% or less range

Embedded electronic systems driven by run-time reconfigurable hardware

Abstract This doctoral thesis addresses the design of embedded electronic systems based on run-time reconfigurable hardware technology –available through SRAM-based FPGA/SoC devices– aimed at contributing to enhance the life quality of the human beings. This work does research on the conception of the system architecture and the reconfiguration engine that provides to the FPGA the capability of dynamic partial reconfiguration in order to synthesize, by means of hardware/software co-design, a given application partitioned in processing tasks which are multiplexed in time and space, optimizing thus its physical implementation –silicon area, processing time, complexity, flexibility, functional density, cost and power consumption– in comparison with other alternatives based on static hardware (MCU, DSP, GPU, ASSP, ASIC, etc.). The design flow of such technology is evaluated through the prototyping of several engineering applications (control systems, mathematical coprocessors, complex image processors, etc.), showing a high enough level of maturity for its exploitation in the industry.Resumen Esta tesis doctoral abarca el diseño de sistemas electrónicos embebidos basados en tecnología hardware dinámicamente reconfigurable –disponible a través de dispositivos lógicos programables SRAM FPGA/SoC– que contribuyan a la mejora de la calidad de vida de la sociedad. Se investiga la arquitectura del sistema y del motor de reconfiguración que proporcione a la FPGA la capacidad de reconfiguración dinámica parcial de sus recursos programables, con objeto de sintetizar, mediante codiseño hardware/software, una determinada aplicación particionada en tareas multiplexadas en tiempo y en espacio, optimizando así su implementación física –área de silicio, tiempo de procesado, complejidad, flexibilidad, densidad funcional, coste y potencia disipada– comparada con otras alternativas basadas en hardware estático (MCU, DSP, GPU, ASSP, ASIC, etc.). Se evalúa el flujo de diseño de dicha tecnología a través del prototipado de varias aplicaciones de ingeniería (sistemas de control, coprocesadores aritméticos, procesadores de imagen, etc.), evidenciando un nivel de madurez viable ya para su explotación en la industria.Resum Aquesta tesi doctoral està orientada al disseny de sistemes electrònics empotrats basats en tecnologia hardware dinàmicament reconfigurable –disponible mitjançant dispositius lògics programables SRAM FPGA/SoC– que contribueixin a la millora de la qualitat de vida de la societat. S’investiga l’arquitectura del sistema i del motor de reconfiguració que proporcioni a la FPGA la capacitat de reconfiguració dinàmica parcial dels seus recursos programables, amb l’objectiu de sintetitzar, mitjançant codisseny hardware/software, una determinada aplicació particionada en tasques multiplexades en temps i en espai, optimizant així la seva implementació física –àrea de silici, temps de processat, complexitat, flexibilitat, densitat funcional, cost i potència dissipada– comparada amb altres alternatives basades en hardware estàtic (MCU, DSP, GPU, ASSP, ASIC, etc.). S’evalúa el fluxe de disseny d’aquesta tecnologia a través del prototipat de varies aplicacions d’enginyeria (sistemes de control, coprocessadors aritmètics, processadors d’imatge, etc.), demostrant un nivell de maduresa viable ja per a la seva explotació a la indústria

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

Soft-Error Resilience Framework For Reliable and Energy-Efficient CMOS Logic and Spintronic Memory Architectures

The revolution in chip manufacturing processes spanning five decades has proliferated high performance and energy-efficient nano-electronic devices across all aspects of daily life. In recent years, CMOS technology scaling has realized billions of transistors within large-scale VLSI chips to elevate performance. However, these advancements have also continually augmented the impact of Single-Event Transient (SET) and Single-Event Upset (SEU) occurrences which precipitate a range of Soft-Error (SE) dependability issues. Consequently, soft-error mitigation techniques have become essential to improve systems\u27 reliability. Herein, first, we proposed optimized soft-error resilience designs to improve robustness of sub-micron computing systems. The proposed approaches were developed to deliver energy-efficiency and tolerate double/multiple errors simultaneously while incurring acceptable speed performance degradation compared to the prior work. Secondly, the impact of Process Variation (PV) at the Near-Threshold Voltage (NTV) region on redundancy-based SE-mitigation approaches for High-Performance Computing (HPC) systems was investigated to highlight the approach that can realize favorable attributes, such as reduced critical datapath delay variation and low speed degradation. Finally, recently, spin-based devices have been widely used to design Non-Volatile (NV) elements such as NV latches and flip-flops, which can be leveraged in normally-off computing architectures for Internet-of-Things (IoT) and energy-harvesting-powered applications. Thus, in the last portion of this dissertation, we design and evaluate for soft-error resilience NV-latching circuits that can achieve intriguing features, such as low energy consumption, high computing performance, and superior soft errors tolerance, i.e., concurrently able to tolerate Multiple Node Upset (MNU), to potentially become a mainstream solution for the aerospace and avionic nanoelectronics. Together, these objectives cooperate to increase energy-efficiency and soft errors mitigation resiliency of larger-scale emerging NV latching circuits within iso-energy constraints. In summary, addressing these reliability concerns is paramount to successful deployment of future reliable and energy-efficient CMOS logic and spintronic memory architectures with deeply-scaled devices operating at low-voltages