Novel Computing Paradigms using Oscillators

This dissertation is concerned with new ways of using oscillators to perform computational tasks. Specifically, it introduces methods for building finite state machines (for general-purpose Boolean computation) as well as Ising machines (for solving combinatorial optimization problems) using coupled oscillator networks.But firstly, why oscillators? Why use them for computation?An important reason is simply that oscillators are fascinating. Coupled oscillator systems often display intriguing synchronization phenomena where spontaneous patterns arise. From the synchronous flashing of fireflies to Huygens' clocks ticking in unison, from the molecular mechanism of circadian rhythms to the phase patterns in oscillatory neural circuits, the observation and study of synchronization in coupled oscillators has a long and rich history. Engineers across many disciplines have also taken inspiration from these phenomena, e.g., to design high-performance radio frequency communication circuits and optical lasers. To be able to contribute to the study of coupled oscillators and leverage them in novel paradigms of computing is without question an interesting andfulfilling quest in and of itself.Moreover, as Moore's Law nears its limits, new computing paradigms that are different from mere conventional complementary metal–oxide–semiconductor (CMOS) scaling have become an important area of exploration. One broad direction aims to improve CMOS performance using device technology such as fin field-effect transistors (FinFET) and gate-all-around (GAA) FETs. Other new computing schemes are based on non-CMOS material and device technology, e.g., graphene, carbon nanotubes, memristive devices, optical devices, etc.. Another growing trend in both academia and industry is to build digital application-specific integrated circuits (ASIC) suitable for speeding up certain computational tasks, often leveraging the parallel nature of unconventional non-von Neumann architectures. These schemes seek to circumvent the limitations posed at the device level through innovations at the system/architecture level.Our work on oscillator-based computation represents a direction that is different from the above and features several points of novelty and attractiveness. Firstly, it makes meaningful use of nonlinear dynamical phenomena to tackle well-defined computational tasks that span analog and digital domains. It also differs from conventional computational systems at the fundamental logic encoding level, using timing/phase of oscillation as opposed to voltage levels to represent logic values. These differences bring about several advantages. The change of logic encoding scheme has several device- and system-level benefits related to noise immunity and interference resistance. The use of nonlinear oscillator dynamics allows our systems to address problems difficult for conventional digital computation. Furthermore, our schemes are amenable to realizations using almost all types of oscillators, allowing a wide variety of devices from multiple physical domains to serve as the substrate for computing. This ability to leverage emerging multiphysics devices need not put off the realization of our ideas far into the future. Instead, implementations using well-established circuit technology are already both practical and attractive.This work also differs from all past work on oscillator-based computing, which mostly focuses on specialized image preprocessing tasks, such as edge detection, image segmentation and pattern recognition. Perhaps its most unique feature is that our systems use transitions between analog and digital modes of operation --- unlike other existing schemes that simply couple oscillators and let their phases settle to a continuum of values, we use a special type of injection locking to make each oscillator settle to one of the several well-defined multistable phase-locked states, which we use to encode logic values for computation. Our schemes of oscillator-based Boolean and Ising computation are built upon this digitization of phase; they expand the scope of oscillator-based computing significantly.Our ideas are built on years of past research in the modelling, simulation and analysis of oscillators. While there is a considerable amount of literature (arguably since Christiaan Huygens wrote about his observation of synchronized pendulum clocks in the 17th century) analyzing the synchronization phenomenon from different perspectives at different levels, we have been able to further develop the theory of injection locking, connecting the dots to find a path of analysis that starts from the low-level differential equations of individual oscillators and arrives at phase-based models and energy landscapes of coupled oscillator systems. This theoretical scaffolding is able not only to explain the operation of oscillator-based systems, but also to serve as the basis for simulation and design tools. Building on this, we explore the practical design of our proposed systems, demonstrate working prototypes, as well as develop the techniques, tools and methodologies essential for the process

Heartbeat Optical Coherence Tomography

__Abstract__ Coronary artery disease (CAD) is a manifestation of atherosclerosis, a systemic inflammatory disease of the arteries that causes the formation of plaques in the artery walls. Different plaques may give rise to different symptoms: a gradual narrowing (stenosis) of the vessel by growth of a fibrous or calcific plaque will cause chest pain (due to cardiac ischemia) upon exertion, a condition called stable CAD. Sudden onset of chest pain, or chest pain at rest, is a symptom of unstable CAD or acute coronary syndrome (ACS), which is associated with thrombus formation on plaques, mostly due to rupture of a lipid-core lesion. The most severe form of this disease may lead to a myocardial infarction or heart attack

HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference

Secure two-party computation with homomorphic encryption (HE) protects data privacy with a formal security guarantee but suffers from high communication overhead. While previous works, e.g., Cheetah, Iron, etc, have proposed efficient HE-based protocols for different neural network (NN) operations, they still assume high precision, e.g., fixed point 37 bit, for the NN operations and ignore NNs' native robustness against quantization error. In this paper, we propose HEQuant, which features low-precision-quantization-aware optimization for the HE-based protocols. We observe the benefit of a naive combination of quantization and HE quickly saturates as bit precision goes down. Hence, to further improve communication efficiency, we propose a series of optimizations, including an intra-coefficient packing algorithm and a quantization-aware tiling algorithm, to simultaneously reduce the number and precision of the transferred data. Compared with prior-art HE-based protocols, e.g., CrypTFlow2, Cheetah, Iron, etc, HEQuant achieves $3.5\sim 23.4\times$ communication reduction and $3.0\sim 9.3\times$ latency reduction. Meanwhile, when compared with prior-art network optimization frameworks, e.g., SENet, SNL, etc, HEQuant also achieves $3.1\sim 3.6\times$ communication reduction