A hardware design methodology or platform is complete if it has the capabilities to successfully implement clock tree, predict the power consumption for cases like best and worst Parasitic Interconnect Corners (RC Corners), supply power to every standard cell, etc.This thesis has tried to solve the three unsolved engineering problems in SiLago design. First, power characterization of the flat design which was designed using the SiLago methodology. Second, designing a hierarchical clock tree and harden it inside the SiLago logic. Third, dimensioning hierarchical power grids. Out of these, clock tree illustrates some interesting characteristics as it is programmable and predictable.The tools used for digital designing are Cadence Innovus, Synopsys Design Vision, and Mentor Graphics Questasim. These are very sophisticated tools and widely accepted in industries as well as in academia.The work done in this thesis has enabled SiLago platform one step forward toward its fruition. / En hårdvarudesign metodologi eller plattform är komplett om den har kapabiliteten till att lyckas genomföra klockträdet, förutsäga strömförbrukningen för bästa och värsta fall av Parasitic Interconnect Corners (RC Corners), tillföra kraft till varje standardcell, etc. Denna avhandling har försökt lösa de tre olösta tekniska problemen i SiLago-designen. Det första är strömkvalificering av designen som designades med hjälp av SiLago metoden. Det andra problemet är att designa ett hierarkiskt klockträd och härda det inuti SiLago logik. Det tredje problemet är att dimensionera hierarkiska strömnät. Ur dessa illustrerar klockträdet några intressanta egenskaper eftersom det är programmerbart och förutsägbart. De verktyg som används för digital design är Cadence Innovus, Synopsys Design Visionoch Mentor Graphics Questasim. Dessa verktyg är mycket sofistikerade och allmänt accepterade i industrier såväl som i akademin. Arbetet i denna avhandling har gjort det möjligt för SiLago-plattformen att ta ett steg mot att realiseras.

<p dir="ltr">Conjugated polymers are organic macromolecules that are characterized by a backbone chain of alternating double- and single-bonds. This alternating pattern results in delocalized π electronic systems, contributing to electronic conduction. In the solid state, conjugated polymers exhibit weak intermolecular interactions, rendering them soft nature in comparison to many of their inorganic counterparts, such as silicon, which consist of ‘hard' three-dimensional networks of rigid covalent bonds. In electrolyte, this weak intermolecular interaction creates free pathways for ion penetration and facilitates mixed ionic-electronic coupling. The ionic-electronic coupling of conjugated polymers impacts nearly all their properties, including light absorption, electronic conductivity, mechanical strength, etc.</p><p dir="ltr">Organic iono-optoelectronics represent a class of devices where the ionic-electronic coupling in conjugated polymers can be synergistically or independently controlled by light irradiation and electrical voltage, enabling multimode electronic and optical functionalities. This dissertation explores two types of organic iono-optoelectronic devices: electrochromic devices and artificial eyes. In electrochromic devices, the ionic-electronic coupling is dynamically modulated by electrical voltage, which induces optical changes of conjugated polymers for applications in information visualization, thermal management, camouflage, etc. Conversely, artificial eyes utilize optical stimulation to tailor the electronic-ionic coupling, with electrical potential changes serving as readout. This paradigm shift opens the door to the development of light-driven biomedical electronics and intelligent visual systems. In the development of electrochromic devices, we introduce two strategies that expand the color palette and enhance the optical control of electrochromic devices, promoting their potential use in display and camouflage. In the development of artificial eye development, we introduce an electrochemical transistor device with integrated functions of light perception, memorization, and recognition by leveraging photon-modulated ion-electronic coupling. This device demonstrates great potential for intelligent visual systems and promises future optoelectronic neural interfaces.</p>

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<p>    An all-additive manufacturing technique was developed to print piezoelectrically active polymeric materials, primarily poly(vinylidene fluoride) (PVdF), for use in pressure sensors in soft robotics. The research proceeded in three stages. The initial stage used Fused Deposition Modeling (FDM) and electric poling independently to create piezoelectric PVdF pressure sensors. The second stage merged FDM and electric poling processes. The third stage introduced electrospinning to create flexible, high-output piezoelectric PVdF materials, which were combined with three-dimensional (3D) printed soft structures for stretchable pressure sensors.</p> <p>    The main achievement of the research was the development of the Electric Poling-assisted Additive Manufacturing (EPAM) technique, combining electric poling and FDM 3D printing to print piezoelectric materials with custom structures at lower costs. β-phase in semicrystalline PVdF materials is mainly responsible for piezoelectricity. A higher β-phase content results in superior sensor performance. This technique was evaluated by measuring the piezoelectric output voltage of the printed PVdF films, and β-phase content was quantified using Fourier-transform Infrared spectroscopy (FTIR). The developed EPAM technique was combined with Direct Ink Writing (DIW), becoming a hybrid 3D printing technique. This is the first demonstration of applying a hybrid printing technique to print piezoelectric PVdF-based sensors directly. The sensor was constructed using FDM printed PVdF film as the dielectric sandwiched between two parallel DIW printed silver electrodes. The PVdF sensors have both piezoelectric pressure sensing and capacitive temperature sensing functionalities. The application of the capacitive temperature sensor was demonstrated by applying heating-and-cooling cycles while measuring the capacitance as a function of temperature at a constant frequency, showing improved sensitivities at higher frequencies (i.e., 105 Hz) after dielectric polarization.</p> <p>    The third stage of research was motivated by the need for soft piezoelectric pressure sensors for soft robotics. Challenges were twofold: requiring soft piezoelectric materials with high coefficients for excellent sensors and fabrication techniques to incorporate soft materials into designed structures. Inspired by the EPAM technique, a method combining electrospinning and DIW was used to create soft piezoelectric PVdF/thermal plastic polyurethane (TPU) blend microfiber-based pressure sensors. The soft sensor was integrated with an FDM printed soft structure for a stretchable pressure sensor with both piezoelectric sensing and capacitive sensing mechanisms.</p>

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<p>To facilitate indoor air quality (IAQ) monitoring, the research described herein develops and implements methods for the electronic integration of two types of gas sensor, each functionalized with a polymer blend tailored for CO₂ detection. A highly sensitive and tunable electronic chemiresistive sensor interface was developed and experimentally validated. This device achieved analog-to-digital conversion (ADC) through a pulse width modulated (PWM) signal, temporary data storage with an efficient data buffering system, and noise reduction and signal amplification utilizing an instrumentation amplifier integrator circuit. These techniques can used beyond CO₂-specific applications to compensate for certain undesirable chemiresistive sensor characteristics, such as low response magnitude and signal noise. Additionally, resonant mass sensing circuitry was combined with an on-chip field programmable gate array (FPGA) implemented frequency counter. Hz-level resolution was achieved with an Alorium Snō FPGA board and a Verilog data acquisition and communication program. This device can monitor up to 16 sensor channels simultaneously and has a straightforward interface with a controllable output. Furthermore, the functionality of each integrated sensor was experimentally validated. With additional work, these integrated designs have the potential to be inexpensive, low-power, highly sensitive devices that are suitable for practical use in IAQ monitoring applications.</p>

<p>Tactile speech communication allows individuals to understand speech by sensations transmitted through the sense of touch. Devices that enable tactile speech communication can be an effective means to transmit important messages when the visual and/or auditory systems are overloaded or impaired. This has applications in silent communication and for people with hearing and/or visual impairments. An effective artificial speech communication system must be learned in a reasonable time and be easily remembered. Moreover, it must transmit any word at suitable rates for speech communication. The pursuit of a system that fulfills these requirements is a complex task that requires work in different areas. This thesis presents advancements in four of them. First is the matter of encoding speech information. Here, a phonemic-based approach allowed participants to recognize of tactile phonemes, words, phrases and full sentences. Second is the issue of training users in the use of the system. To this end, this thesis investigated the phenomenon of incidental categorization of vibrotactile stimuli as the foundation of more natural methods to learn a tactile speech communication system. Third is the matter of the neural processing of the tactile speech information. Here, an exploration of the functional characteristics of the phonemic-based approach using EEG was conducted. Finally, there is the matter of implementing the system for consumer use. In this area, this work addresses practical considerations of delivering rich haptic effects with current wearable technologies. These are informative for the design of actuators used in tactile speech communication devices.</p>

<p>The rapid expansion of consumer Internet-of-Things (IoT) technology across various application domains has made it one of the most sought-after and swiftly evolving technologies. IoT devices offer numerous benefits, such as enhanced security, convenience, and cost reduction. However, as these devices need access to sensitive aspects of human life to function effectively, their abuse can lead to significant financial, psychological, and physical harm. While previous studies have examined the vulnerabilities of IoT devices, insufficient research has delved into the impact and mitigation of threats to users' privacy and safety. This dissertation addresses the challenge of protecting user safety and privacy against threats posed by IoT device vulnerabilities. We first introduce a novel IWMD architecture, which serves as the last line of defense against unsafe operations of Implantable and Wearable Medical Devices (IWMDs). We demonstrate the architecture's effectiveness through a prototype artificial pancreas. Subsequent chapters emphasize the safety and privacy of smart home device users. First, we propose a unique device activity-based categorization and learning approach for network traffic analysis. Utilizing this technology, we present a new smart home security framework and a device type identification mechanism to enhance transparency and access control in smart home device communication. Lastly, we propose a novel traffic shaping technique that hinders adversaries from discerning user activities through traffic analysis. Experiments conducted on commercially available IoT devices confirm that our solutions effectively address these issues with minimal overhead.</p>

<p>Convolutional neural networks (CNNs) have become important workloads due to their<br> impressive accuracy in tasks like image classification and recognition. Convolution operations<br> are compute intensive, and this cost profoundly increases with newer and better CNN models.<br> However, convolutions come with characteristics such as sparsity which can be exploited. In<br> this dissertation, we propose three different works to capture sparsity for faster performance<br> and reduced energy. </p> <p><br></p> <p>The first work is an accelerator design called <em>SparTen</em> for improving two-<br> sided sparsity (i.e, sparsity in both filters and feature maps) convolutions with fine-grained<br> sparsity. <em>SparTen</em> identifies efficient inner join as the key primitive for hardware acceleration<br> of sparse convolution. In addition, <em>SparTen</em> proposes load balancing schemes for higher<br> compute unit utilization. <em>SparTen</em> performs 4.7x, 1.8x and 3x better than dense architecture,<br> one-sided architecture and SCNN, the previous state of the art accelerator. The second work<br> <em>BARISTA</em> scales up SparTen (and SparTen like proposals) to large-scale implementation<br> with as many compute units as recent dense accelerators (e.g., Googles Tensor processing<br> unit) to achieve full speedups afforded by sparsity. However at such large scales, buffering,<br> on-chip bandwidth, and compute utilization are highly intertwined where optimizing for<br> one factor strains another and may invalidate some optimizations proposed in small-scale<br> implementations. <em>BARISTA</em> proposes novel techniques to balance the three factors in large-<br> scale accelerators. <em>BARISTA</em> performs 5.4x, 2.2x, 1.7x and 2.5x better than dense, one-<br> sided, naively scaled two-sided and an iso-area two-sided architecture, respectively. The last<br> work, <em>EUREKA</em> builds an efficient tensor core to execute dense, structured and unstructured<br> sparsity with losing efficiency. <em>EUREKA</em> achieves this by proposing novel techniques to<br> improve compute utilization by slightly tweaking operand stationarity. <em>EUREKA</em> achieves a<br> speedup of 5x, 2.5x, along with 3.2x and 1.7x energy reductions over Dense and structured<br> sparse execution respectively. <em>EUREKA</em> only incurs area and power overheads of 6% and<br> 11.5%, respectively, over Ampere</p>