An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.Comment: To appear at the DSN 2020 conferenc

Parallel Architectures for Many-Core Systems-On-Chip in Deep Sub-Micron Technology

Despite the several issues faced in the past, the evolutionary trend of silicon has kept its constant pace. Today an ever increasing number of cores is integrated onto the same die. Unfortunately, the extraordinary performance achievable by the many-core paradigm is limited by several factors. Memory bandwidth limitation, combined with inefficient synchronization mechanisms, can severely overcome the potential computation capabilities. Moreover, the huge HW/SW design space requires accurate and flexible tools to perform architectural explorations and validation of design choices. In this thesis we focus on the aforementioned aspects: a flexible and accurate Virtual Platform has been developed, targeting a reference many-core architecture. Such tool has been used to perform architectural explorations, focusing on instruction caching architecture and hybrid HW/SW synchronization mechanism. Beside architectural implications, another issue of embedded systems is considered: energy efficiency. Near Threshold Computing is a key research area in the Ultra-Low-Power domain, as it promises a tenfold improvement in energy efficiency compared to super-threshold operation and it mitigates thermal bottlenecks. The physical implications of modern deep sub-micron technology are severely limiting performance and reliability of modern designs. Reliability becomes a major obstacle when operating in NTC, especially memory operation becomes unreliable and can compromise system correctness. In the present work a novel hybrid memory architecture is devised to overcome reliability issues and at the same time improve energy efficiency by means of aggressive voltage scaling when allowed by workload requirements. Variability is another great drawback of near-threshold operation. The greatly increased sensitivity to threshold voltage variations in today a major concern for electronic devices. We introduce a variation-tolerant extension of the baseline many-core architecture. By means of micro-architectural knobs and a lightweight runtime control unit, the baseline architecture becomes dynamically tolerant to variations

Circuits and Systems Advances in Near Threshold Computing

Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing

An experimental study of reduced-voltage operation in modern FPGAs for neural network acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect ofenvironmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W ) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.The work done for this paper was partially supported by a HiPEAC Collaboration Grant funded by the H2020 HiPEAC Project under grant agreement No. 779656. The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the LEGaTO Project (www.legato-project.eu), grant agreement No. 780681.Peer ReviewedPostprint (author's final draft

Variation-Tolerant and Voltage-Scalable Integrated Circuits Design

Ultra-low-voltage (ULV) operation where the supply voltage of the digital computing hardware is scaled down to the level near or below transistor threshold voltage (e.g. 300-500mV) is a key technique to achieve high computing energy efficiency. It has enabled many new exciting applications in the field of Internet of Things (IoT) devices and energy-constrained applications such as medical implants, environment sensors, and micro-robots. Ultra-low-voltage (ULV) operation is also commonly used with the emerging architectures that are often non Von-Neumann style to empower energy-efficient cognitive computing. One the biggest challenge in realizing ULV design is the large circuit delay variability. To guarantee functionality in the worst-case process, voltage, and temperature (PVT) condition, the traditional safety margin approach requires operating at a slower clock frequency or higher supply voltage which significantly limits the achievable energy efficiency of the hardware. To fully claim the energy efficiency of ULV, the large circuit delay variation needs to be adaptively handled. However, the existing adaptive techniques that are optimized for nominal supply voltage operation and traditional Von-Neumann architectures become inefficient for ULV designs and emerging architectures. This thesis presents adaptive techniques based on timing error detection and correction (EDAC) that are more suitable for the energy-constrained ULV designs and the emerging architectures. The proposed techniques are demonstrated in three test chips: (1) R-Processor: A 0.4V resilient processor with a voltage-scalable and low-overhead in-situ EDAC technique. It achieves 38% energy efficiency improvement or 2.3X throughput improvement as compared to the traditional safety margin approach. (2) A 450mV timing-margin-free waveform sorter for brain computer interface (BCI) microsystem. It achieves 49.3% higher energy efficiency and 35.6% higher throughput than the traditional safety margin approach. (3) Ultra-low-power and robust power-management system which consists of a microprocessor employing ULV EDAC, 63-ratio integrated switched-capacitor DC-DC converter, and a fully-digital error based regulation controller. In this thesis, we also explore circuits for emerging techniques. The first is temperature sensors for dynamic-thermal-management (DTM). The modern high-performance microprocessors suffer from ever-increasing power densities which has led to reliability concerns and increased cooling costs from excessive heat. In order to monitor and manage the thermal behavior, DTM techniques embed multiple temperature sensors and use its information. The size, accuracy, and voltage-scalability of the sensor are critical for the performance of DTM. Therefore, we propose a temperature sensor that directly senses transistor threshold voltage and the test chip demonstrates 9X smaller area, 3X higher accuracy, and 200mV lower voltage scalability (down to 400mV) than the previous state-of-art. Another area of exploration is interconnect design for ultra-dynamic-voltage-scaling (UDVS) systems. UDVS has been proposed for applications that require both high performance and high energy efficiency. UDVS can provide peak performance with nominal supply voltage when work load is high. When work load is moderate or low, UDVS systems can switch to ULV operation for higher energy efficiency. One of the critical challenges for developing UDVS systems is the inflexibility in various circuit fabrics that are often optimized for a single supply voltage. One critical example is conventional repeater based long interconnects which suffers from non-optimal performance and energy efficiency in UDVS systems. Therefore, in this thesis, we propose a reconfigurable interconnect design based on regenerators and demonstrate near optimal performance and energy efficiency across the supply voltage of 0.3V and 1V

Exceeding Conservative Limits: A Consolidated Analysis on Modern Hardware Margins

Modern large-scale computing systems (data centers, supercomputers, cloud and edge setups and high-end cyber-physical systems) employ heterogeneous architectures that consist of multicore CPUs, general-purpose many-core GPUs, and programmable FPGAs. The effective utilization of these architectures poses several challenges, among which a primary one is power consumption. Voltage reduction is one of the most efficient methods to reduce power consumption of a chip. With the galloping adoption of hardware accelerators (i.e., GPUs and FPGAs) in large datacenters and other large-scale computing infrastructures, a comprehensive evaluation of the safe voltage reduction levels for each different chip can be employed for efficient reduction of the total power. We present a survey of recent studies in voltage margins reduction at the system level for modern CPUs, GPUs and FPGAs. The pessimistic voltage guardbands inserted by the silicon vendors can be exploited in all devices for significant power savings. On average, voltage reduction can reach 12% in multicore CPUs, 20% in manycore GPUs and 39% in FPGAs.Comment: Accepted for publication in IEEE Transactions on Device and Materials Reliabilit

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Designing energy-efficient computing systems using equalization and machine learning

As technology scaling slows down in the nanometer CMOS regime and mobile computing becomes more ubiquitous, designing energy-efficient hardware for mobile systems is becoming increasingly critical and challenging. Although various approaches like near-threshold computing (NTC), aggressive voltage scaling with shadow latches, etc. have been proposed to get the most out of limited battery life, there is still no “silver bullet” to increasing power-performance demands of the mobile systems. Moreover, given that a mobile system could operate in a variety of environmental conditions, like different temperatures, have varying performance requirements, etc., there is a growing need for designing tunable/reconfigurable systems in order to achieve energy-efficient operation. In this work we propose to address the energy- efficiency problem of mobile systems using two different approaches: circuit tunability and distributed adaptive algorithms. Inspired by the communication systems, we developed feedback equalization based digital logic that changes the threshold of its gates based on the input pattern. We showed that feedback equalization in static complementary CMOS logic enabled up to 20% reduction in energy dissipation while maintaining the performance metrics. We also achieved 30% reduction in energy dissipation for pass-transistor digital logic (PTL) with equalization while maintaining performance. In addition, we proposed a mechanism that leverages feedback equalization techniques to achieve near optimal operation of static complementary CMOS logic blocks over the entire voltage range from near threshold supply voltage to nominal supply voltage. Using energy-delay product (EDP) as a metric we analyzed the use of the feedback equalizer as part of various sequential computational blocks. Our analysis shows that for near-threshold voltage operation, when equalization was used, we can improve the operating frequency by up to 30%, while the energy increase was less than 15%, with an overall EDP reduction of ≈10%. We also observe an EDP reduction of close to 5% across entire above-threshold voltage range. On the distributed adaptive algorithm front, we explored energy-efficient hardware implementation of machine learning algorithms. We proposed an adaptive classifier that leverages the wide variability in data complexity to enable energy-efficient data classification operations for mobile systems. Our approach takes advantage of varying classification hardness across data to dynamically allocate resources and improve energy efficiency. On average, our adaptive classifier is ≈100× more energy efficient but has ≈1% higher error rate than a complex radial basis function classifier and is ≈10× less energy efficient but has ≈40% lower error rate than a simple linear classifier across a wide range of classification data sets. We also developed a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) under tight energy budgets. The FoG architecture takes advantage of the fact that in random forests a small portion of the weak classifiers (decision trees) might be sufficient to achieve high statistical performance. By dividing the random forest into smaller forests (Groves), and conditionally executing the rest of the forest, FoG is able to achieve much higher energy efficiency levels for comparable error rates. We also take advantage of the distributed nature of the FoG to achieve high level of parallelism. Our evaluation shows that at maximum achievable accuracies FoG consumes ≈1.48×, ≈24×, ≈2.5×, and ≈34.7× lower energy per classification compared to conventional RF, SVM-RBF , Multi-Layer Perceptron Network (MLP), and CNN, respectively. FoG is 6.5× less energy efficient than SVM-LR, but achieves 18% higher accuracy on average across all considered datasets

Design and Optimization for Resilient Energy Efficient Computing

Heutzutage sind moderne elektronische Systeme ein integraler Bestandteil unseres Alltags. Dies wurde unter anderem durch das exponentielle Wachstum der Integrationsdichte von integrierten Schaltkreisen ermöglicht zusammen mit einer Verbesserung der Energieeffizienz, welche in den letzten 50 Jahren stattfand, auch bekannt als Moore‘s Gesetz. In diesem Zusammenhang ist die Nachfrage von energieeffizienten digitalen Schaltkreisen enorm angestiegen, besonders in Anwendungsfeldern wie dem Internet of Things (IoT). Da der Leistungsverbrauch von Schaltkreisen stark mit der Versorgungsspannung verknüpft ist, wurden effiziente Verfahren entwickelt, welche die Versorgungsspannung in den nahen Schwellenspannung-Bereich skalieren, zusammengefasst unter dem Begriff Near-Threshold-Computing (NTC). Mithilfe dieser Verfahren kann eine Erhöhung der Energieeffizienz von Schaltungen um eine ganze Größenordnung ermöglicht werden. Neben der verbesserten Energiebilanz ergeben sich jedoch zahlreiche Herausforderungen was den Schaltungsentwurf angeht. Zum Beispiel führt das Reduzieren der Versorgungsspannung in den nahen Schwellenspannungsbereich zu einer verzehnfachten Erhöhung der Sensibilität der Schaltkreise gegenüber Prozessvariation, Spannungsfluktuationen und Temperaturveränderungen. Die Einflüsse dieser Variationen reduzieren die Zuverlässigkeit von NTC Schaltkreisen und sind ihr größtes Hindernis bezüglich einer umfassenden Nutzung. Traditionelle Ansätze und Methoden aus dem nominalen Spannungsbereich zur Kompensation von Variabilität können nicht effizient angewandt werden, da die starken Performance-Variationen und Sensitivitäten im nahen Schwellenspannungsbereich dessen Kapazitäten übersteigen. Aus diesem Grund sind neue Entwurfsparadigmen und Entwurfsautomatisierungskonzepte für die Anwendung von NTC erforderlich. Das Ziel dieser Arbeit ist die zuvor erwähnten Probleme durch die Bereitstellung von ganzheitlichen Methoden zum Design von NTC Schaltkreisen sowie dessen Entwurfsautomatisierung anzugehen, welche insbesondere auf der Schaltungs- sowie Logik-Ebene angewandt werden. Dabei werden tiefgehende Analysen der Zuverlässigkeit von NTC Systemen miteinbezogen und Optimierungsmethoden werden vorgeschlagen welche die Zuverlässigkeit, Performance und Energieeffizienz verbessern. Die Beiträge dieser Arbeit sind wie folgt: Schaltungssynthese und Timing Closure unter Einbezug von Variationen: Das Einhalten von Anforderungen an das zeitliche Verhalten und Zuverlässigkeit von NTC ist eine anspruchsvolle Aufgabe. Die Auswirkungen von Variabilität kommen bei starken Performance-Schwankungen, welche zu teuren zeitlichen Sicherheitsmargen führen, oder sich in Hold-Time Verstößen ausdrücken, verursacht durch funktionale Störungen, zum Vorschein. Die konventionellen Ansätze beschränken sich dabei alleine auf die Erhöhung von zeitlichen Sicherheitsmargen. Dies ist jedoch sehr ineffizient für NTC, wegen dem starken Ausmaß an Variationen und den erhöhten Leckströmen. In dieser Arbeit wird ein Konzept zur Synthese und Timing Closure von Schaltkreisen unter Variationen vorgestellt, welches sowohl die Sensitivität gegenüber Variationen reduziert als auch die Energieeffizienz, Performance und Zuverlässigkeit verbessert und zugleich den Mehraufwand von Timing Closures [1, 2] verringert. Simulationsergebnisse belegen, dass unser vorgeschlagener Ansatz die Verzögerungszeit um 87% reduziert und die Performance und Energieeffizienz um 25% beziehungsweise 7.4% verbessert, zu Kosten eines erhöhten Flächenbedarfs von 4.8%. Schichtübergreifende Zuverlässigkeits-, Energieeffizienz- und Performance-Optimierung von Datenpfaden: Schichtübergreifende Analyse von Prozessor-Datenpfaden, welche den ganzen Weg spannen vom Kompilierer zum Schaltungsentwurf, kann potenzielle Optimierungsansätze aufzeigen. Ein Datenpfad ist eine Kombination von mehreren funktionalen Einheiten, welche diverse Instruktionen verarbeiten können. Unsere Analyse zeigt, dass die Ausführungszeiten von Instruktionen bei niedrigen Versorgungsspannungen stark variieren, weshalb eine Klassifikation in schnelle und langsame Instruktionen vorgenommen werden kann. Des Weiteren können funktionale Instruktionen als häufig und selten genutzte Instruktionen kategorisiert werden. Diese Arbeit stellt eine Multi-Zyklen-Instruktionen-Methode vor, welche die Energieeffizienz und Belastbarkeit von funktionalen Einheiten erhöhen kann [3]. Zusätzlich stellen wir einen Partitionsalgorithmus vor, welcher ein fein-granulares Power-gating von selten genutzten Einheiten ermöglicht [4] durch Partition von einzelnen funktionalen Einheiten in mehrere kleinere Einheiten. Die vorgeschlagenen Methoden verbessern das zeitliche Schaltungsverhalten signifikant, und begrenzen zugleich die Leckströme beträchtlich, durch Einsatz einer Kombination von Schaltungs-Redesign- und Code-Replacement-Techniken. Simulationsresultate zeigen, dass die entwickelten Methoden die Performance und Energieeffizienz von arithmetisch-logischen Einheiten (ALU) um 19% beziehungsweise 43% verbessern. Des Weiteren kann der Zuwachs in Performance der optimierten Schaltungen in eine Verbesserung der Zuverlässigkeit umgewandelt werden [5, 6]. Post-Fabrication und Laufzeit-Tuning: Prozess- und Laufzeitvariationen haben einen starken Einfluss auf den Minimum Energy Point (MEP) von NTC-Schaltungen, welcher mit der energieeffizientesten Versorgungsspannung assoziiert ist. Es ist ein besonderes Anliegen, die NTC-Schaltung nach der Herstellung (post-fabrication) so zu kalibrieren, dass sich die Schaltung im MEP-Zustand befindet, um die beste Energieeffizient zu erreichen. In dieser Arbeit, werden Post-Fabrication und Laufzeit-Tuning vorgeschlagen, welche die Schaltung basierend auf Geschwindigkeits- und Leistungsverbrauch-Messungen nach der Herstellung auf den MEP kalibrieren. Die vorgestellten Techniken ermitteln den MEP per Chip-Basis um den Einfluss von Prozessvariationen mit einzubeziehen und dynamisch die Versorgungsspannung und Frequenz zu adaptieren um zeitabhängige Variationen wie Workload und Temperatur zu adressieren. Zu diesem Zweck wird in die Firmware eines Chips ein Regression-Modell integriert, welches den MEP basierend auf Workload- und Temperatur-Messungen zur Laufzeit extrahiert. Das Regressions-Modell ist für jeden Chip einzigartig und basiert lediglich auf Post-Fabrication-Messungen. Simulationsergebnisse zeigen das der entwickelte Ansatz eine sehr hohe prognostische Treffsicherheit und Energieeffizienz hat, ähnlich zu hardware-implementierten Methoden, jedoch ohne hardware-seitigen Mehraufwand [7, 8]. Selektierte Flip-Flop Optimierung: Ultra-Low-Voltage Schaltungen müssen im nominalen Versorgungsspannungs-Mode arbeiten um zeitliche Anforderungen von laufenden Anwendungen zu erfüllen. In diesem Fall ist die Schaltung von starken Alterungsprozessen betroffen, welche die Transistoren durch Erhöhung der Schwellenspannungen degradieren. Unsere tiefgehenden Analysen haben gezeigt das gewisse Flip-Flop-Architekturen von diesen Alterungserscheinungen beeinflusst werden indem fälschlicherweise konstante Werte ( \u270\u27 oder \u271\u27) für eine lange Zeit gespeichert sind. Im Vergleich zu anderen Komponenten sind Flip-Flops sensitiver zu Alterungsprozessen und versagen unter anderem dabei einen neuen Wert innerhalb des vorgegebenen zeitlichen Rahmens zu übernehmen. Außerdem kann auch ein geringfügiger Spannungsabfall zu diesen zeitlichen Verstößen führen, falls die betreffenden gealterten Flip-Flops zum kritischen Pfad zuzuordnen sind. In dieser Arbeit wird eine selektiver Flip-Flop-Optimierungsmethode vorgestellt, welche die Schaltungen bezüglich Robustheit gegen statische Alterung und Spannungsabfall optimieren. Dabei werden zuerst optimierte robuste Flip-Flops generiert und diese dann anschließend in die Standard-Zellen-Bibliotheken integriert. Flip-Flops, die in der Schaltung zum kritischen Pfad gehören und Alterung sowie Spannungsabfall erfahren, werden durch die optimierten robusten Versionen ersetzt, um das Zeitverhalten und die Zuverlässigkeit der Schaltung zu verbessern [9, 10]. Simulationsergebnisse zeigen, dass die erwartete Lebenszeit eines Prozessors um 37% verbessert werden kann, während Leckströme um nur 0.1% erhöht werden. Während NTC das Potenzial hat große Energieeffizienz zu ermöglichen, ist der Einsatz in neue Anwendungsfeldern wie IoT wegen den zuvor erwähnten Problemen bezüglich der hohen Sensitivität gegenüber Variationen und deshalb mangelnder Zuverlässigkeit, noch nicht durchsetzbar. In dieser Dissertation und in noch nicht publizierten Werken [11–17], stellen wir Lösungen zu diesen Problemen vor, die eine Integration von NTC in heutige Systeme ermöglichen