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

Fabrication of a thin silver nanowire composite film and investigation of a patterning technique.

Electrically conductive polymers encompass an exciting field of research for applications in dye-sensitized solar cells (DSSC’s). DSSC’s possess several advantages over other types of solar cells. They offer the potential for high quantum efficiency, solar conversion efficiency approaching that of traditional silicon panels, rapid charge transfer kinetics for photo-excited electrons, mechanical flexibility, and cost efficient manufacturing processes. However, key drawbacks to their large scale production and performance lifetime lie in their reliance on costly indium tin oxide (ITO), fluorinated tin oxide (FTO), and platinum for electrode materials, and the mechanical fragility inherent to a liquid electrolyte layer component. Much of the current research in the field concerns identifying an effective solid material to replace the liquid electrolyte presently used in DSSC’s. Within this field of research, electrically conductive polymers (ECP’s) have attracted much interest. One such ECP, PEDOT:PSS [poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)] has a combination of high flexibility, electrical conductivity, and optical transmittance, making it a viable candidate. Another subset of this research field involves qualifying a cheaper candidate for replacing the costly platinum electrode of DSSC’s. Metallic nanoparticles, whose characteristic dimension is on the order of one to hundreds of nanometers, possess unique physical properties emergent at their size scale. Particularly, nanowires of highly conductive metals such as silver deposited on the surface of ECP’s raises the possibility of an electrode made using simple bulk processing techniques and using drastically lower quantities of expensive materials for the electrode than typical for DSSC’s while offering the same level of conductivity. Furthermore, an interesting challenge lies in patterning the nanowire network using a simple bulk driving force. Achieving facile nanowire alignment, and thus anisotropic electrical conductivity, opens the door to a variety of applications extending beyond solar cell electrodes, such as flexible nanoscale circuitry. The present thesis describes and evaluates the physical properties of composite thin films of PEDOT:PSS with silver nanowire networks. A simple laboratory scale method for creating a randomly aligned silver nanowire network on PEDOT:PSS is first studied, revealing impressive conductivity increases on the order of 120 versus the bare PEDOT:PSS film. Sheet resistances of the composite films average 37 Ω/□ with average ultraviolet and visible light (UV-Vis) transmittances of 63%. UV-Vis transmittance correlates inversely with the surface concentration of nanowires as expected, with a power regression fit to the data (R2 = 0.96). Of note, no strong correlation is detected between sheet resistance and nanowire surface concentration within the range tested. Additionally, a method for aligning silver nanowires on PEDOT:PSS using a magnetic field is explored. Unfortunately, no significant anisotropy in conductivity is measured using the conditions outlined in these experiments, and explanations are discussed leading into recommendations for future work

Roadmap on photonic metasurfaces

Funding: C.R. and U.L. acknowledge support through the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy via the Excellence Cluster 3D Matter Made to Order (EXC-2082/1, Grant No. 390761711). A.B.E. acknowledges support through the Cluster of Excellence PhoenixD (EXC 2122, Project ID No. 390833453). I.F.-C. and C.R. acknowledge support through the CRC Waves: Analysis and Numerics (SFB 1173, Grant No. 258734477. K.A. acknowledges funding from the Swiss National Science Foundation (Project No. PZ00P2_193221).Here we present a roadmap on Photonic metasurfaces. This document consists of a number of perspective articles on different applications, challenge areas or technologies underlying photonic metasurfaces. Each perspective will introduce the topic, present a state of the art as well as give an insight into the future direction of the subfield.Peer reviewe

Multi-parameter optimisation of quantum optical systems

Quantum optical systems are poised to become integral components of technologies of the future. While there is growing commercial interest in these systems---for applications in information processing, secure communication and precision metrology---there remain significant technical challenges to overcome before widespread adoption is possible. In this thesis we consider the general problem of optimising quantum optical systems, with a focus on sensing and information processing applications. We investigate four different classes of system with varying degrees of generality and complexity, and demonstrate four corresponding optimisation techniques. At the most specific end of the spectrum---where behaviour is best understood---we consider the problem of interferometric sensitivity enhancement, specifically in the context of long-baseline gravitational wave detectors. We investigate the use of an auxiliary optomechanical system to generate squeezed light exhibiting frequency-dependent quadrature rotation. Such rotation is necessary to evade the effect of quantum back action and achieve broadband sensitivity beyond the standard quantum limit. We find that a cavity optomechanical system is generally unsuitable for this purpose, since the quadrature rotation occurs in the opposite direction to that required for broadband sensitivity improvement. Next we introduce a general technique to engineer arbitrary optical spring potentials in cavity optomechanical systems. This technique has the potential to optimise many types of sensors relying on the optical spring effect. As an example, we show that this technique could yield an enhancement in sensitivity by a factor of 5 when applied to a certain gravitational sensor based on a levitated cavity mirror. We then consider a particular nanowire-based optomechanical system with potential applications in force sensing. We demonstrate a variety of ways to improve its sensitivity to transient forces. We first apply a non-stationary feedback cooling protocol to the system, and achieve an improvement in peak signal-to-noise ratio by a factor of 3, corresponding to a force resolution of 0.2fN. We then implement two non-stationary estimation schemes, which involve post-processing data taken in the absence of physical feedback cooling, to achieve a comparable enhancement in performance without the need for additional experimental complexity. Finally, to address the most complex of systems, we present a general-purpose machine learning algorithm capable of automatically modelling and optimising arbitrary physical systems without human input. To demonstrate the potential of the algorithm we apply it to a magneto-optical trap used for a quantum memory, and achieve an improvement in optical depth from 138 to 448. The four techniques presented differ significantly in their style and the types of systems to which they are applicable. Successfully harnessing the full range of such optimisation procedures will be vital in unlocking the potential of quantum optical systems in the technologies of the futur

Stoquasticity in circuit QED

We analyze whether circuit-QED Hamiltonians are stoquastic focusing on systems of coupled flux qubits: we show that scalable sign-problem free path integral Monte Carlo simulations can typically be performed for such systems. Despite this, we corroborate the recent finding [arXiv:1903.06139] that an effective, non-stoquastic qubit Hamiltonian can emerge in a system of capacitively coupled flux qubits. We find that if the capacitive coupling is sufficiently small, this non-stoquasticity of the effective qubit Hamiltonian can be avoided if we perform a canonical transformation prior to projecting onto an effective qubit Hamiltonian. Our results shed light on the power of circuit-QED Hamiltonians for the use of quantum adiabatic computation and the subtlety of finding a representation which cures the sign problem in these system

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p