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Machine Learning-Based Monte Carlo Denoising

Brief description not available

Deep Learning-Based Approach to Accelerate T cell Receptor Design

Researchers at the University of California, Davis have developed a deep learning simulation model to predict mutated T-cell receptor affinity and avidity for immunotherapy applications.

Workflow to Computationally Optimize Upcycling of Critical Metals from Spent Lit

This technology computationally optimizes the upcycling of critical metals in deep eutectic solvents with molecular dynamics, artificial intelligence, and experimental approaches.

Cascaded Resonant Switched-Capacitor For Power Converter Architecture

Data center power demands are growing fast. To address this situation, next-generation data centers are moving to 48 V bus architectures to reduce distribution loss on the bus bar of server racks. One important research topic regarding this architecture is stepping down from 48 V to the point-of-load voltage, which is usually implemented by an intermediate bus converter followed by a voltage regulator, with the benefits of high efficiency and reutilization of 12 V legacy systems.Many topologies have been explored for the 48-to-12 V intermediate bus applications, such as inductor-based converters. However, since capacitors have higher energy densities compared with inductors, switched-capacitor based converters have the potential to achieve higher power density and have gained increasing attention in performance-driven applications. Integrating resonant conductors into cascaded switch-capacitor converters further improves performance.To address this potential, researchers at UC Berkeley developed a novel resonant switched-capacitor based converter. The Berkeley converter uses a simple structure and operation principle, and has the potential to achieve dramatic efficiency and power density improvement over existing leading alternatives.

Adapting Existing Computer Networks to a Quantum-Based Internet Future

Researchers at the University of California, Davis have developed an approach for integrating quantum computers into the existing internet backbone.

Reducing Electrical Current Variations in Phase-Locked Loop Systems

Researchers at the University of California, Davis have developed a method of eliminating electrical current mismatches in the charge pumps of phase-locked loops (PLL) systems - thereby increasing their power efficiency and phase detection capabilities.

DNA-based, Read-Only Memory (ROM) for Data Storage Applications

Researchers at the University of California, Davis have collaborated with colleagues at the University of Washington and Emory University to develop a DNA-based, memory and data storage technology that integrates seamlessly with semiconductor-based technologies and conventional electronic devices.

Smart Suction Cup for Adaptive Gripping and Haptic Exploration

Vacuum grippers are widely used in industry to handle objects via suction pressure. Unicontact suction cups are commonly used for gripping because they are simple to operate and can handle a variety of items, including those that are delicate, large, or inaccessible to jaw grippers. However, suction cup grippers have challenges such as planning a contact location and inertial force-induced grasping failure. To address these challenges, UC Berkeley researchers developed a tactile sensing technology for smart suction cups. This Berkeley sensing technology can detect suction contact and prevent suction cup grasp failures. It can perform tactile sensing of object properties such as roughness or porosity that might lead to grasping failures before they happen. If a grasp failure does happen, the technology gains additional information about why and how the failure occurred to prevent similar failures in future attempts. Sensing occurs quickly, such that robot behavior can remain fast while increasing performance, efficiency and reliability.  As compared with other robotic grasping sensing technologies, this smart suction cup technology is affordable, resilient and easy to service. The cup is manufactured using the same process as other suction cups, and electronics are simple and located away from the point-of-contact and protected from damage or hazardous exposure.

Multi-Phase Hybrid Power Converter Architecture With Large Conversion Ratios

The power demands on data centers are large and increasing rapidly. This is straining data center economic and environment impacts, and in turn driving improvements in data center power efficiencies. Data centers have been widely adopting 48 V intermediate bus architectures due to higher efficiency, good flexibility, and reduced cost. However, a major challenge in such systems is the conversion from the 48 V bus to the extreme low voltage and high current operating levels of server CPUs and GPUs.To address this challenge, UC Berkeley researchers developed a multi-phase hybrid power converter architecture. The Berkeley design uses hybrid converter topologies. A switched-capacitor network is smartly merged with a switched-inductor network, resulting in circuit component number reduction and soft-charging operation of the capacitors. Furthermore, the Berkeley architecture integrates a multi-phase control technique to achieve a higher conversion ratio of the switched-capacitor network, which can further improve the overall system efficiency without increasing the circuit size.  

New Technique to Reduce Register File Accesses in GPUs

Prof. Nael Ghazaleh and Hodjat Asghari Esfeden from the University of California, Riverside have developed Breathing Operand Windows (BOW), an enhanced GPU pipeline and operand collector technique that supports bypassing register file accesses and instead passes values directly between instructions within the same window. While this baseline design can only bypass register reads, they also introduce an improved design capable of bypassing unnecessary write operations to the RF. Compiler optimizations help guide the write-back destination of operands depending on whether they will be reused to further reduce the write traffic. The BOW microarchitecture reduces RF dynamic energy consumption by 55%, while at the same time increases overall performance by 11%, with a modest overhead of 12KB of additional storage which is ~4% of the RF size. Fig 1: shows the dynamic energy normalized to the baseline GPU for BOW-WR across fifteen different benchmarks. The small segments on top of each bar represent the overheads of the structures added by the idea. Dynamic energy savings in Fig 1 are due to the reduced number of accesses to the register file as BOW-WR shields the RF from unnecessary read and write operations.  

Systems and Methods for Sound-Enhanced Meeting Platforms

Computer-based, internet-connected, audio/video meeting platforms have become pervasive worldwide, especially since the 2020 emergence of the COVID-19 pandemic lockdown. These meeting platforms include Cisco Webex, Google Meet, GoTo, Microsoft Teams, and Zoom. However, those popular platforms are optimized for meetings in which all the participants are attending the meeting online, individually. Accordingly, those platforms have shortcomings when used for hybrid meetings in which some participants are attending together in-person and others attending online. Also, the existing platforms are problematic for large meetings in big rooms (e.g. classrooms) in which most or all of the participants are in-person. To address those suboptimal meet platform situations, researchers at UC Berkeley conceived systems, methods, algorithms and other software for a meeting platform that's optimized for hybrid meetings and large in-person meetings. The Berkeley meeting platform offers a user experience that's familiar to users of the conventional meeting platforms. Also, the Berkeley platform doesn't require any specialized participant hardware or specialized physical room infrastructure (beyond standard internet connectivity).

Embedded Power Amplifier

Researchers at the University of California, Davis have developed an amplifier technology that boosts power output in order to improve data transmission speeds for high-frequency communications.

Contextual Augmentation Using Scene Graphs

Spatial computing experiences are constrained by the real-world surroundings of the user.  In such experiences, augmenting virtual objects to existing scenes require a contextual approach, where geometrical conflicts are avoided, and functional and plausible relationships to other objects are maintained in the target environment.  Yet, due to the complexity and diversity of user environments, automatically calculating ideal positions of virtual content that is adaptive to the context of the scene is considered a challenging task.    UC researchers have developed a framework which augments scenes with virtual objects using an explicit generative model to learn topological relationship from priors extracted from a real-world and/or synthetic 3D datasets.  Primarily designed for spatial computing applications, SceneGen extracts features from rooms into a novel spatial representation which encapsulates positional and orientational relationships of a scene which captures pairwise topology between objects, object groups, and the room.  The AR application iteratively augments objects by sampling positions and orientations across a room to create a probabilistic heat map of where the object can be placed.  By placing objects in poses where the spatial relationships are likely, we are able to augment scenes that are realistic. 

Virtual Reality For Anhedonia Program

UCLA researchers in the Department of Psychology have developed a behavioral training program for the improvement of anhedonia.

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM

UCLA researchers in the Department of Mathematicshave developed a method to maintain data privacy.

Machine Learning Program that Diagnoses Hypoadrenocorticism in Dogs Using Standard Blood Test Results

Researchers at the University of California, Davis have developed a program based on machine learning algorithms to aid in diagnosing hypoadrenocorticism.

Applying a Machine Learning Algorithm to Canine Radiographs for Automated Detection of Left Atrial Enlargement

Researchers at the University of California, Davis have developed a method of detecting canine left atrial enlargement as an early sign of mitral valve disease by applying machine learning techniques to thoracic radiograph images.

Development of a CMOS-Compatible, Nano-photonic, Laser

Researchers at the University of California, Davis have developed a new class of lasers and amplifiers that uses a CMOS-compatible electronics platform - and can also be applied to nano-amplifiers and nano-lasers applications.

Deep Learning Network and Compression Framework over Limited Bandwidth Network Links

Researchers at the University of California, Davis have developed a technology that enables the quantization of discrete wavelet transformed coefficients to reduce bandwidth for cloud-based storage applications. 

Athermal Nanophotonic Lasers

Researchers at the University of California, Davis have developed a nanolaser platform built from materials that do not exhibit optical gain.

Multi-Wavelength, Nanophotonic, Neural Computing System

Researchers at the University of California, Davis have developed a multi-wavelength, Spiking, Nanophotonic, Neural Reservoir Computing (SNNRC) system with high-dimensional (HD) computing capability.

New Classes Of Cage And Polyhedron And New Classes Of Nanotube And Nanotube With Planar Faces

UCLA researchers have developed a novel algorithm that can be used to design unique self-assembled molecules and nanostructures.

Thermodynamic Integration Simulation Method for Filling Molecular Enclosures Using Spliced Soft-Core Interaction Potential

Researchers have developed a simulation method to determine the properties of molecular enclosures based on slow growth thermodynamic integration (SGTI).

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