Reliable Low-Power High Performance Spintronic Memories

Moores Gesetz folgend, ist es der Chipindustrie in den letzten fünf Jahrzehnten gelungen, ein explosionsartiges Wachstum zu erreichen. Dies hatte ebenso einen exponentiellen Anstieg der Nachfrage von Speicherkomponenten zur Folge, was wiederum zu speicherlastigen Chips in den heutigen Computersystemen führt. Allerdings stellen traditionelle on-Chip Speichertech- nologien wie Static Random Access Memories (SRAMs), Dynamic Random Access Memories (DRAMs) und Flip-Flops eine Herausforderung in Bezug auf Skalierbarkeit, Verlustleistung und Zuverlässigkeit dar. Eben jene Herausforderungen und die überwältigende Nachfrage nach höherer Performanz und Integrationsdichte des on-Chip Speichers motivieren Forscher, nach neuen nichtflüchtigen Speichertechnologien zu suchen. Aufkommende spintronische Spe- ichertechnologien wie Spin Orbit Torque (SOT) und Spin Transfer Torque (STT) erhielten in den letzten Jahren eine hohe Aufmerksamkeit, da sie eine Reihe an Vorteilen bieten. Dazu gehören Nichtflüchtigkeit, Skalierbarkeit, hohe Beständigkeit, CMOS Kompatibilität und Unan- fälligkeit gegenüber Soft-Errors. In der Spintronik repräsentiert der Spin eines Elektrons dessen Information. Das Datum wird durch die Höhe des Widerstandes gespeichert, welche sich durch das Anlegen eines polarisierten Stroms an das Speichermedium verändern lässt. Das Prob- lem der statischen Leistung gehen die Speichergeräte sowohl durch deren verlustleistungsfreie Eigenschaft, als auch durch ihr Standard- Aus/Sofort-Ein Verhalten an. Nichtsdestotrotz sind noch andere Probleme, wie die hohe Zugriffslatenz und die Energieaufnahme zu lösen, bevor sie eine verbreitete Anwendung finden können. Um diesen Problemen gerecht zu werden, sind neue Computerparadigmen, -architekturen und -entwurfsphilosophien notwendig. Die hohe Zugriffslatenz der Spintroniktechnologie ist auf eine vergleichsweise lange Schalt- dauer zurückzuführen, welche die von konventionellem SRAM übersteigt. Des Weiteren ist auf Grund des stochastischen Schaltvorgangs der Speicherzelle und des Einflusses der Prozessvari- ation ein nicht zu vernachlässigender Zeitraum dafür erforderlich. In diesem Zeitraum wird ein konstanter Schreibstrom durch die Bitzelle geleitet, um den Schaltvorgang zu gewährleisten. Dieser Vorgang verursacht eine hohe Energieaufnahme. Für die Leseoperation wird gleicher- maßen ein beachtliches Zeitfenster benötigt, ebenfalls bedingt durch den Einfluss der Prozess- variation. Dem gegenüber stehen diverse Zuverlässigkeitsprobleme. Dazu gehören unter An- derem die Leseintereferenz und andere Degenerationspobleme, wie das des Time Dependent Di- electric Breakdowns (TDDB). Diese Zuverlässigkeitsprobleme sind wiederum auf die benötigten längeren Schaltzeiten zurückzuführen, welche in der Folge auch einen über längere Zeit an- liegenden Lese- bzw. Schreibstrom implizieren. Es ist daher notwendig, sowohl die Energie, als auch die Latenz zur Steigerung der Zuverlässigkeit zu reduzieren, um daraus einen potenziellen Kandidaten für ein on-Chip Speichersystem zu machen. In dieser Dissertation werden wir Entwurfsstrategien vorstellen, welche das Ziel verfolgen, die Herausforderungen des Cache-, Register- und Flip-Flop-Entwurfs anzugehen. Dies erre- ichen wir unter Zuhilfenahme eines Cross-Layer Ansatzes. Für Caches entwickelten wir ver- schiedene Ansätze auf Schaltkreisebene, welche sowohl auf der Speicherarchitekturebene, als auch auf der Systemebene in Bezug auf Energieaufnahme, Performanzsteigerung und Zuver- lässigkeitverbesserung evaluiert werden. Wir entwickeln eine Selbstabschalttechnik, sowohl für die Lese-, als auch die Schreiboperation von Caches. Diese ist in der Lage, den Abschluss der entsprechenden Operation dynamisch zu ermitteln. Nachdem der Abschluss erkannt wurde, wird die Lese- bzw. Schreiboperation sofort gestoppt, um Energie zu sparen. Zusätzlich limitiert die Selbstabschalttechnik die Dauer des Stromflusses durch die Speicherzelle, was wiederum das Auftreten von TDDB und Leseinterferenz bei Schreib- bzw. Leseoperationen re- duziert. Zur Verbesserung der Schreiblatenz heben wir den Schreibstrom an der Bitzelle an, um den magnetischen Schaltprozess zu beschleunigen. Um registerbankspezifische Anforderungen zu berücksichtigen, haben wir zusätzlich eine Multiport-Speicherarchitektur entworfen, welche eine einzigartige Eigenschaft der SOT-Zelle ausnutzt, um simultan Lese- und Schreiboperatio- nen auszuführen. Es ist daher möglich Lese/Schreib- Konfilkte auf Bitzellen-Ebene zu lösen, was sich wiederum in einer sehr viel einfacheren Multiport- Registerbankarchitektur nieder- schlägt. Zusätzlich zu den Speicheransätzen haben wir ebenfalls zwei Flip-Flop-Architekturen vorgestellt. Die erste ist eine nichtflüchtige non-Shadow Flip-Flop-Architektur, welche die Speicherzelle als aktive Komponente nutzt. Dies ermöglicht das sofortige An- und Ausschalten der Versorgungss- pannung und ist daher besonders gut für aggressives Powergating geeignet. Alles in Allem zeigt der vorgestellte Flip-Flop-Entwurf eine ähnliche Timing-Charakteristik wie die konventioneller CMOS Flip-Flops auf. Jedoch erlaubt er zur selben Zeit eine signifikante Reduktion der statis- chen Leistungsaufnahme im Vergleich zu nichtflüchtigen Shadow- Flip-Flops. Die zweite ist eine fehlertolerante Flip-Flop-Architektur, welche sich unanfällig gegenüber diversen Defekten und Fehlern verhält. Die Leistungsfähigkeit aller vorgestellten Techniken wird durch ausführliche Simulationen auf Schaltkreisebene verdeutlicht, welche weiter durch detaillierte Evaluationen auf Systemebene untermauert werden. Im Allgemeinen konnten wir verschiedene Techniken en- twickeln, die erhebliche Verbesserungen in Bezug auf Performanz, Energie und Zuverlässigkeit von spintronischen on-Chip Speichern, wie Caches, Register und Flip-Flops erreichen

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

Design and Robustness Analysis on Non-volatile Storage and Logic Circuit

By combining the flexibility of MOS logic and the non-volatility of spintronic devices, spin-MOS logic and storage circuitry offer a promising approach to implement highly integrated, power-efficient, and nonvolatile computing and storage systems. Besides the persistent errors due to process variations, however, the functional correctness of Spin-MOS circuitry suffers from additional non-persistent errors that are incurred by the randomness of spintronic device operations, i.e., thermal fluctuations. This work quantitatively investigates the impact of thermal fluctuations on the operations of two typical Spin-MOS circuitry: one transistor and one magnetic tunnel junction (1T1J) spin-transfer torque random access memory (STT-RAM) cell and a nonvolatile latch design. A new nonvolatile latch design is proposed based on magnetic tunneling junction (MTJ) devices. In the standby mode, the latched data can be retained in the MTJs without consuming any power. Two types of operation errors can occur, namely, persistent and non-persistent errors. These are quantitatively analyzed by including models for process variations and thermal fluctuations during the read and write operations. A mixture importance sampling methodology is applied to enable yield-driven design and extend its application beyond memories to peripheral circuits and logic blocks. Several possible design techniques to reduce thermal induced non-persistent error rate are also discussed

A low power and soft error resilience guard-gated Quartro-based flip-flop in 45 nm CMOS technology

Abstract Conventional flip‐flops are more vulnerable to particle strikes in a radiation environment. To overcome this disadvantage, in the literature, many radiation‐hardened flip‐flops (FFs) based on techniques like triple modular redundancy, dual interlocked cell, Quatro and guard‐gated Quatro cell, and so on, are discussed. The flip‐flop realized using radiation hardened by design Quatro cell is named as the improved version of Quatro flip‐flop (IVQFF). Single event upset (SEU) at inverter stages of master/slave and at output are the two drawbacks of IVQFF. This study proposes a guard‐gated Quatro FF (GQFF) using guard‐gated Quatro cell and Muller C‐element. To overcome the SEU at inverter stages of IVQFF, in GQFF, the inverter stages are realized in a parallel fashion. A dual‐input Muller C‐element is connected to the GQFF output stage to mask the SEU and thus maintain the correct output. The proposed GQFF tolerates both single node upset (SNU) and double node upset (DNU). It also achieves low power. To prove the efficacy, GQFF and the existing FFs are implemented in 45 nm Complementary Metal Oxide Semiconductor (CMOS) technology. From the simulation results, it may be noted that the GQFF is 100% immune to SNUs and 50% immune to DNUs

Normally-Off Computing and Checkpoint/Rollback for Fast, Low-Power, and Reliable Devices

International audienceSince the advent of complementary metal oxide semiconductors (CMOS), the number of transistors per die has continued to increase, reaching today several billion transistors. As a result, it has been possible to design and fabricate smart devices able to run at high speed. However, the power consumption of systems-on-chip has significantly increased due to the high density integration and the high leakage power of current CMOS transistors. As a result, the limits of heat dissipation make further improvement in performance difficult. A high level of autonomy for battery-powered devices is a real challenge. To deal with these issues, spin-transfer-torque magnetic random-access memory (STT-MRAM) technology is seen as a promising solution. In addition to its attractive performance features, STT-MRAM can bring nonvolatility to a system to allow full data retention after a complete shutdown while maintaining a fast wake-up time. Considering two 32-bit embedded processors, this letter shows how STT-MRAM can improve energy efficiency and reliability of future embedded systems thanks to normally-off computing and checkpointing/rollback techniques. A detailed analysis is performed to evaluate the cost related to the backup/recovery of the system. Index Terms—Spintronic memory and logic, embedded processor, spin-transfer-torque, magnetic random-access memory

Design considerations of a nonvolatile accumulator-based 8-bit processor

The rise of the Internet of Things (IoT) and theconstant growth of portable electronics have leveraged the con-cern with energy consumption. Nonvolatile memory (NVM)emerged as a solution to mitigate the problem due to its abilityto retain data on sleep mode without a power supply. Non-volatile processors (NVPs) may further improve energy savingby using nonvolatile flip-flops (NVFFs) to store system state,allowing the device to be turned off when idle and resume ex-ecution instantly after power-on. In view of the potential pre-sented by NVPs, this work describes the initial steps to imple-ment a nonvolatile version of Neander, a hypothetical processorcreated for educational purposes. First, we implemented Ne-ander in Register Transfer Level (RTL), separating the com-binational logic from the sequential elements. Then, the lat-ter was replaced by circuit-level descriptions of volatile flip-flops. We then validated this implementation by employinga mixed-signal simulation over a set of benchmarks. Resultshave shown the expected behavior for the whole instructionset. Then, we implemented circuit-level descriptions of mag-netic tunnel junction (MTJ) based nonvolatile flip-flops, usingan open-source MTJ model. These elements were exhaustivelyvalidated using electrical simulations. With these results, weintend to carry on the implementation and fully equip our pro-cessor with nonvolatile features such as instant wake-up

Energy and Area Efficient Machine Learning Architectures using Spin-Based Neurons

Recently, spintronic devices with low energy barrier nanomagnets such as spin orbit torque-Magnetic Tunnel Junctions (SOT-MTJs) and embedded magnetoresistive random access memory (MRAM) devices are being leveraged as a natural building block to provide probabilistic sigmoidal activation functions for RBMs. In this dissertation research, we use the Probabilistic Inference Network Simulator (PIN-Sim) to realize a circuit-level implementation of deep belief networks (DBNs) using memristive crossbars as weighted connections and embedded MRAM-based neurons as activation functions. Herein, a probabilistic interpolation recoder (PIR) circuit is developed for DBNs with probabilistic spin logic (p-bit)-based neurons to interpolate the probabilistic output of the neurons in the last hidden layer which are representing different output classes. Moreover, the impact of reducing the Magnetic Tunnel Junction\u27s (MTJ\u27s) energy barrier is assessed and optimized for the resulting stochasticity present in the learning system. In p-bit based DBNs, different defects such as variation of the nanomagnet thickness can undermine functionality by decreasing the fluctuation speed of the p-bit realized using a nanomagnet. A method is developed and refined to control the fluctuation frequency of the output of a p-bit device by employing a feedback mechanism. The feedback can alleviate this process variation sensitivity of p-bit based DBNs. This compact and low complexity method which is presented by introducing the self-compensating circuit can alleviate the influences of process variation in fabrication and practical implementation. Furthermore, this research presents an innovative image recognition technique for MNIST dataset on the basis of p-bit-based DBNs and TSK rule-based fuzzy systems. The proposed DBN-fuzzy system is introduced to benefit from low energy and area consumption of p-bit-based DBNs and high accuracy of TSK rule-based fuzzy systems. This system initially recognizes the top results through the p-bit-based DBN and then, the fuzzy system is employed to attain the top-1 recognition results from the obtained top outputs. Simulation results exhibit that a DBN-Fuzzy neural network not only has lower energy and area consumption than bigger DBN topologies while also achieving higher accuracy

Heterogeneous Reconfigurable Fabrics for In-circuit Training and Evaluation of Neuromorphic Architectures

A heterogeneous device technology reconfigurable logic fabric is proposed which leverages the cooperating advantages of distinct magnetic random access memory (MRAM)-based look-up tables (LUTs) to realize sequential logic circuits, along with conventional SRAM-based LUTs to realize combinational logic paths. The resulting Hybrid Spin/Charge FPGA (HSC-FPGA) using magnetic tunnel junction (MTJ) devices within this topology demonstrates commensurate reductions in area and power consumption over fabrics having LUTs constructed with either individual technology alone. Herein, a hierarchical top-down design approach is used to develop the HSCFPGA starting from the configurable logic block (CLB) and slice structures down to LUT circuits and the corresponding device fabrication paradigms. This facilitates a novel architectural approach to reduce leakage energy, minimize communication occurrence and energy cost by eliminating unnecessary data transfer, and support auto-tuning for resilience. Furthermore, HSC-FPGA enables new advantages of technology co-design which trades off alternative mappings between emerging devices and transistors at runtime by allowing dynamic remapping to adaptively leverage the intrinsic computing features of each device technology. HSC-FPGA offers a platform for fine-grained Logic-In-Memory architectures and runtime adaptive hardware. An orthogonal dimension of fabric heterogeneity is also non-determinism enabled by either low-voltage CMOS or probabilistic emerging devices. It can be realized using probabilistic devices within a reconfigurable network to blend deterministic and probabilistic computational models. Herein, consider the probabilistic spin logic p-bit device as a fabric element comprising a crossbar-structured weighted array. The Programmability of the resistive network interconnecting p-bit devices can be achieved by modifying the resistive states of the array\u27s weighted connections. Thus, the programmable weighted array forms a CLB-scale macro co-processing element with bitstream programmability. This allows field programmability for a wide range of classification problems and recognition tasks to allow fluid mappings of probabilistic and deterministic computing approaches. In particular, a Deep Belief Network (DBN) is implemented in the field using recurrent layers of co-processing elements to form an n x m1 x m2 x ::: x mi weighted array as a configurable hardware circuit with an n-input layer followed by i ≥ 1 hidden layers. As neuromorphic architectures using post-CMOS devices increase in capability and network size, the utility and benefits of reconfigurable fabrics of neuromorphic modules can be anticipated to continue to accelerate