Security Aspects of Printed Electronics Applications

Gedruckte Elektronik (Printed Electronics (PE)) ist eine neu aufkommende Technologie welche komplementär zu konventioneller Elektronik eingesetzt wird. Dessen einzigartigen Merkmale führten zu einen starken Anstieg von Marktanteilen, welche 2010 \$6 Milliarden betrugen, \$41 Milliarden in 2019 und in 2027 geschätzt \$153 Milliarden. Gedruckte Elektronik kombiniert additive Technologien mit funktionalen Tinten um elektronische Komponenten aus verschiedenen Materialien direkt am Verwendungsort, kosteneffizient und umweltfreundlich herzustellen. Die dabei verwendeten Substrate können flexibel, leicht, transparent, großflächig oder implantierbar sein. Dadurch können mit gedruckter Elektronik (noch) visionäre Anwendungen wie Smart-Packaging, elektronische Einmalprodukte, Smart Labels oder digitale Haut realisiert werden. Um den Fortschritt von gedruckten Elektronik-Technologien voranzutreiben, basierten die meisten Optimierungen hauptsächlich auf der Erhöhung von Produktionsausbeute, Reliabilität und Performance. Jedoch wurde auch die Bedeutung von Sicherheitsaspekten von Hardware-Plattformen in den letzten Jahren immer mehr in den Vordergrund gerückt. Da realisierte Anwendungen in gedruckter Elektronik vitale Funktionalitäten bereitstellen können, die sensible Nutzerdaten beinhalten, wie zum Beispiel in implantierten Geräten und intelligenten Pflastern zur Gesundheitsüberwachung, führen Sicherheitsmängel und fehlendes Produktvertrauen in der Herstellungskette zu teils ernsten und schwerwiegenden Problemen. Des Weiteren, wegen den charakteristischen Merkmalen von gedruckter Elektronik, wie zum Beispiel additive Herstellungsverfahren, hohe Strukturgröße, wenige Schichten und begrenzten Produktionsschritten, ist gedruckte Hardware schon per se anfällig für hardware-basierte Attacken wie Reverse-Engineering, Produktfälschung und Hardware-Trojanern. Darüber hinaus ist die Adoption von Gegenmaßnahmen aus konventionellen Technologien unpassend und ineffizient, da solche zu extremen Mehraufwänden in der kostengünstigen Fertigung von gedruckter Elektronik führen würden. Aus diesem Grund liefert diese Arbeit eine Technologie-spezifische Bewertung von Bedrohungen auf der Hardware-Ebene und dessen Gegenmaßnahmen in der Form von Ressourcen-beschränkten Hardware-Primitiven, um die Produktionskette und Funktionalitäten von gedruckter Elektronik-Anwendungen zu schützen. Der erste Beitrag dieser Dissertation ist ein vorgeschlagener Ansatz um gedruckte Physical Unclonable Functions (pPUF) zu entwerfen, welche Sicherheitsschlüssel bereitstellen um mehrere sicherheitsrelevante Gegenmaßnahmen wie Authentifizierung und Fingerabdrücke zu ermöglichen. Zusätzlich optimieren wir die multi-bit pPUF-Designs um den Flächenbedarf eines 16-bit-Schlüssels-Generators um 31\% zu verringern. Außerdem entwickeln wir ein Analyse-Framework basierend auf Monte Carlo-Simulationen für pPUFs, mit welchem wir Simulationen und Herstellungs-basierte Analysen durchführen können. Unsere Ergebnisse haben gezeigt, dass die pPUFs die notwendigen Eigenschaften besitzen um erfolgreich als Sicherheitsanwendung eingesetzt zu werden, wie Einzigartigkeit der Signatur und ausreichende Robustheit. Der Betrieb der gedruckten pPUFs war möglich bis zu sehr geringen Betriebsspannungen von nur 0.5 V. Im zweiten Beitrag dieser Arbeit stellen wir einen kompakten Entwurf eines gedruckten physikalischen Zufallsgenerator vor (True Random Number Generator (pTRNG)), welcher unvorhersehbare Schlüssel für kryptographische Funktionen und zufälligen "Authentication Challenges" generieren kann. Der pTRNG Entwurf verbessert Prozess-Variationen unter Verwendung von einer Anpassungsmethode von gedruckten Widerständen, ermöglicht durch die individuelle Konfigurierbarkeit von gedruckten Schaltungen, um die generierten Bits nur von Zufallsrauschen abhängig zu machen, und damit ein echtes Zufallsverhalten zu erhalten. Die Simulationsergebnisse legen nahe, dass die gesamten Prozessvariationen des TRNGs um das 110-fache verbessert werden, und der zufallsgenerierte Bitstream der TRNGs die "National Institute of Standards and Technology Statistical Test Suit"-Tests bestanden hat. Auch hier können wir nachweisen, dass die Betriebsspannungen der TRNGs von mehreren Volt zu nur 0.5 V lagen, wie unsere Charakterisierungsergebnisse der hergestellten TRNGs aufgezeigt haben. Der dritte Beitrag dieser Dissertation ist die Beschreibung der einzigartigen Merkmale von Schaltungsentwurf und Herstellung von gedruckter Elektronik, welche sehr verschieden zu konventionellen Technologien ist, und dadurch eine neuartige Reverse-Engineering (RE)-Methode notwendig macht. Hierfür stellen wir eine robuste RE-Methode vor, welche auf Supervised-Learning-Algorithmen für gedruckte Schaltungen basiert, um die Vulnerabilität gegenüber RE-Attacken zu demonstrieren. Die RE-Ergebnisse zeigen, dass die vorgestellte RE-Methode auf zahlreiche gedruckte Schaltungen ohne viel Komplexität oder teure Werkzeuge angewandt werden kann. Der letzte Beitrag dieser Arbeit ist ein vorgeschlagenes Konzept für eine "one-time programmable" gedruckte Look-up Table (pLUT), welche beliebige digitale Funktionen realisieren kann und Gegenmaßnahmen unterstützt wie Camouflaging, Split-Manufacturing und Watermarking um Attacken auf der Hardware-Ebene zu verhindern. Ein Vergleich des vorgeschlagenen pLUT-Konzepts mit existierenden Lösungen hat gezeigt, dass die pLUT weniger Flächen-bedarf, geringere worst-case Verzögerungszeiten und Leistungsverbrauch hat. Um die Konfigurierbarkeit der vorgestellten pLUT zu verifizieren, wurde es simuliert, hergestellt und programmiert mittels Tintenstrahl-gedruckter elektrisch leitfähiger Tinte um erfolgreich Logik-Gatter wie XNOR, XOR und AND zu realisieren. Die Simulation und Charakterisierungsergebnisse haben die erfolgreiche Funktionalität der pLUT bei Betriebsspannungen von nur 1 V belegt

EIE: Efficient Inference Engine on Compressed Deep Neural Network

State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power. Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x; Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x. Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102GOPS/s working directly on a compressed network, corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is 24,000x and 3,400x more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy efficiency and area efficiency.Comment: External Links: TheNextPlatform: http://goo.gl/f7qX0L ; O'Reilly: https://goo.gl/Id1HNT ; Hacker News: https://goo.gl/KM72SV ; Embedded-vision: http://goo.gl/joQNg8 ; Talk at NVIDIA GTC'16: http://goo.gl/6wJYvn ; Talk at Embedded Vision Summit: https://goo.gl/7abFNe ; Talk at Stanford University: https://goo.gl/6lwuer. Published as a conference paper in ISCA 201

DSENT - A Tool Connecting Emerging Photonics with Electronics for Opto-Electronic Networks-on-Chip Modeling

With the advent of many-core chips that place substantial demand on the NoC, photonics has been investigated as a promising alternative to electrical NoCs. While numerous opto-electronic NoCs have been proposed, their evaluations tend to be based on fixed numbers for both photonic and electrical components, making it difficult to co-optimize. Through our own forays into opto-electronic NoC design, we observe that photonics and electronics are very much intertwined, reflecting a strong need for a NoC modeling tool that accurately models parameterized electronic and photonic components within a unified framework, capturing their interactions faithfully. In this paper, we present a tool, DSENT, for design space exploration of electrical and opto-electrical networks. We form a framework that constructs basic NoC building blocks from electrical and photonic technology parameters. To demonstrate potential use cases, we perform a network case study illustrating data-rate tradeoffs, a comparison with scaled electrical technology, and sensitivity to photonics parameters

Design Techniques for Energy-Quality Scalable Digital Systems

Energy efficiency is one of the key design goals in modern computing. Increasingly complex tasks are being executed in mobile devices and Internet of Things end-nodes, which are expected to operate for long time intervals, in the orders of months or years, with the limited energy budgets provided by small form-factor batteries. Fortunately, many of such tasks are error resilient, meaning that they can toler- ate some relaxation in the accuracy, precision or reliability of internal operations, without a significant impact on the overall output quality. The error resilience of an application may derive from a number of factors. The processing of analog sensor inputs measuring quantities from the physical world may not always require maximum precision, as the amount of information that can be extracted is limited by the presence of external noise. Outputs destined for human consumption may also contain small or occasional errors, thanks to the limited capabilities of our vision and hearing systems. Finally, some computational patterns commonly found in domains such as statistics, machine learning and operational research, naturally tend to reduce or eliminate errors. Energy-Quality (EQ) scalable digital systems systematically trade off the quality of computations with energy efficiency, by relaxing the precision, the accuracy, or the reliability of internal software and hardware components in exchange for energy reductions. This design paradigm is believed to offer one of the most promising solutions to the impelling need for low-energy computing. Despite these high expectations, the current state-of-the-art in EQ scalable design suffers from important shortcomings. First, the great majority of techniques proposed in literature focus only on processing hardware and software components. Nonetheless, for many real devices, processing contributes only to a small portion of the total energy consumption, which is dominated by other components (e.g. I/O, memory or data transfers). Second, in order to fulfill its promises and become diffused in commercial devices, EQ scalable design needs to achieve industrial level maturity. This involves moving from purely academic research based on high-level models and theoretical assumptions to engineered flows compatible with existing industry standards. Third, the time-varying nature of error tolerance, both among different applications and within a single task, should become more central in the proposed design methods. This involves designing “dynamic” systems in which the precision or reliability of operations (and consequently their energy consumption) can be dynamically tuned at runtime, rather than “static” solutions, in which the output quality is fixed at design-time. This thesis introduces several new EQ scalable design techniques for digital systems that take the previous observations into account. Besides processing, the proposed methods apply the principles of EQ scalable design also to interconnects and peripherals, which are often relevant contributors to the total energy in sensor nodes and mobile systems respectively. Regardless of the target component, the presented techniques pay special attention to the accurate evaluation of benefits and overheads deriving from EQ scalability, using industrial-level models, and on the integration with existing standard tools and protocols. Moreover, all the works presented in this thesis allow the dynamic reconfiguration of output quality and energy consumption. More specifically, the contribution of this thesis is divided in three parts. In a first body of work, the design of EQ scalable modules for processing hardware data paths is considered. Three design flows are presented, targeting different technologies and exploiting different ways to achieve EQ scalability, i.e. timing-induced errors and precision reduction. These works are inspired by previous approaches from the literature, namely Reduced-Precision Redundancy and Dynamic Accuracy Scaling, which are re-thought to make them compatible with standard Electronic Design Automation (EDA) tools and flows, providing solutions to overcome their main limitations. The second part of the thesis investigates the application of EQ scalable design to serial interconnects, which are the de facto standard for data exchanges between processing hardware and sensors. In this context, two novel bus encodings are proposed, called Approximate Differential Encoding and Serial-T0, that exploit the statistical characteristics of data produced by sensors to reduce the energy consumption on the bus at the cost of controlled data approximations. The two techniques achieve different results for data of different origins, but share the common features of allowing runtime reconfiguration of the allowed error and being compatible with standard serial bus protocols. Finally, the last part of the manuscript is devoted to the application of EQ scalable design principles to displays, which are often among the most energy- hungry components in mobile systems. The two proposals in this context leverage the emissive nature of Organic Light-Emitting Diode (OLED) displays to save energy by altering the displayed image, thus inducing an output quality reduction that depends on the amount of such alteration. The first technique implements an image-adaptive form of brightness scaling, whose outputs are optimized in terms of balance between power consumption and similarity with the input. The second approach achieves concurrent power reduction and image enhancement, by means of an adaptive polynomial transformation. Both solutions focus on minimizing the overheads associated with a real-time implementation of the transformations in software or hardware, so that these do not offset the savings in the display. For each of these three topics, results show that the aforementioned goal of building EQ scalable systems compatible with existing best practices and mature for being integrated in commercial devices can be effectively achieved. Moreover, they also show that very simple and similar principles can be applied to design EQ scalable versions of different system components (processing, peripherals and I/O), and to equip these components with knobs for the runtime reconfiguration of the energy versus quality tradeoff

The self-consistent quantum-electrostatic problem in strongly non-linear regime

The self-consistent quantum-electrostatic (also known as Poisson-Schr\"odinger) problem is notoriously difficult in situations where the density of states varies rapidly with energy. At low temperatures, these fluctuations make the problem highly non-linear which renders iterative schemes deeply unstable. We present a stable algorithm that provides a solution to this problem with controlled accuracy. The technique is intrinsically convergent including in highly non-linear regimes. We illustrate our approach with (i) a calculation of the compressible and incompressible stripes in the integer quantum Hall regime and (ii) a calculation of the differential conductance of a quantum point contact geometry. Our technique provides a viable route for the predictive modeling of the transport properties of quantum nanoelectronics devices.Comment: 28 pages. 14 figures. Added solution to a potential failure mode of the algorith

Automatic controls and regulators: A compilation

Devices, methods, and techniques for control and regulation of the mechanical/physical functions involved in implementing the space program are discussed. Section one deals with automatic controls considered to be, essentially, start-stop operations or those holding the activity in a desired constraint. Devices that may be used to regulate activities within desired ranges or subject them to predetermined changes are dealt with in section two