Graphics Processing Unit-Based Computer-Aided Design Algorithms for Electronic Design Automation

The electronic design automation (EDA) tools are a specific set of software that play important roles in modern integrated circuit (IC) design. These software automate the design processes of IC with various stages. Among these stages, two important EDA design tools are the focus of this research: floorplanning and global routing. Specifically, the goal of this study is to parallelize these two tools such that their execution time can be significantly shortened on modern multi-core and graphics processing unit (GPU) architectures. The GPU hardware is a massively parallel architecture, enabling thousands of independent threads to execute concurrently. Although a small set of EDA tools can benefit from using GPU to accelerate their speed, most algorithms in this field are designed with the single-core paradigm in mind. The floorplanning and global routing algorithms are among the latter, and difficult to render any speedup on the GPU due to their inherent sequential nature. This work parallelizes the floorplanning and global routing algorithm through a novel approach and results in significant speedups for both tools implemented on the GPU hardware. Specifically, with a complete overhaul of solution space and design space exploration, a GPU-based floorplanning algorithm is able to render 4-166X speedup, while achieving similar or improved solutions compared with the sequential algorithm. The GPU-based global routing algorithm is shown to achieve significant speedup against existing state-of-the-art routers, while delivering competitive solution quality. Importantly, this parallel model for global routing renders a stable solution that is independent from the level of parallelism. In summary, this research has shown that through a design paradigm overhaul, sequential algorithms can also benefit from the massively parallel architecture. The findings of this study have a positive impact on the efficiency and design quality of modern EDA design flow

AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems

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A Robust Semantics-based Watermark for Large Language Model against Paraphrasing

Large language models (LLMs) have show great ability in various natural language tasks. However, there are concerns that LLMs are possible to be used improperly or even illegally. To prevent the malicious usage of LLMs, detecting LLM-generated text becomes crucial in the deployment of LLM applications. Watermarking is an effective strategy to detect the LLM-generated content by encoding a pre-defined secret watermark to facilitate the detection process. However, the majority of existing watermark methods leverage the simple hashes of precedent tokens to partition vocabulary. Such watermark can be easily eliminated by paraphrase and correspondingly the detection effectiveness will be greatly compromised. Thus, to enhance the robustness against paraphrase, we propose a semantics-based watermark framework SemaMark. It leverages the semantics as an alternative to simple hashes of tokens since the paraphrase will likely preserve the semantic meaning of the sentences. Comprehensive experiments are conducted to demonstrate the effectiveness and robustness of SemaMark under different paraphrases

Investigation of adhesive joining strategies for the application of a multi-material light rail vehicle

To meet the high demand for lightweight energy-efficient and safe structures for transport applications, a current state-of-the-art light rail vehicle structure is under development that adopts a multi-material design strategy. This strategy creates the need for advanced multi-material joining technologies. The compatibility of the adhesive with a wide range of material types and the possibility of joining multi-material structures is also a key advantage to its success. In this paper, the feasibility of using either epoxy or polyurethane adhesive joining techniques applied to the multi-material vehicle structure is investigated. Importantly, consideration is given to the effect of variation in bond thickness for both families of structural adhesives. Multi-material adhesively bonded single lap joints with different adhesives of controlled bond thicknesses were manufactured and tested in order to experimentally assess the shear strength and stiffness. The torsional stiffness and natural frequency of the vehicle were modelled using a global two-dimensional finite element model (FEM) with different adhesive properties, and the obtained vehicle performances were further explained by the coupon-level experimental tests. The results showed that the vehicle using polyurethane adhesive with a target bond thickness of 1.0 mm allowed for optimal modal frequency and weight reduction

Exploring Memorization in Fine-tuned Language Models

LLMs have shown great capabilities in various tasks but also exhibited memorization of training data, thus raising tremendous privacy and copyright concerns. While prior work has studied memorization during pre-training, the exploration of memorization during fine-tuning is rather limited. Compared with pre-training, fine-tuning typically involves sensitive data and diverse objectives, thus may bring unique memorization behaviors and distinct privacy risks. In this work, we conduct the first comprehensive analysis to explore LMs' memorization during fine-tuning across tasks. Our studies with open-sourced and our own fine-tuned LMs across various tasks indicate that fine-tuned memorization presents a strong disparity among tasks. We provide an understanding of this task disparity via sparse coding theory and unveil a strong correlation between memorization and attention score distribution. By investigating its memorization behavior, multi-task fine-tuning paves a potential strategy to mitigate fine-tuned memorization

An Autonomous Unmanned Aerial Vehicle-Based Imagery System Development and Remote Sensing Images Classification for Agricultural Applications

This work concentrates on the topic of remote sensing using a multispectral imag-ing system for water management and agriculture applications. The platform, which is alight-weight inexpensive runway-free unmanned aerial vehicle (UAV), namely, AggieAir, ispresented initially. A major portion of this work focuses on the development of a light-weight multispectral imager payload for the AggieAir platform, called GhostFoto. Theimager is band-recongurable, covering both visual red, green, and blue (RGB) and nearinfrared (NIR) spectrum, and interfaced with UAV on-board computer. The developmentof the image processing techniques, which are based on the collected multispectral aerialimages, is also presented in this work. One application is to perform fully autonomous rivertracking for applications such as river water management. Simulation based on aerial mul-tispectral images is done to demonstrate the feasibility of the developed algorithm. Othereort is made to create a systematic method to generate normalized difference vegetationindex (NDVI) using the airborne imagery. The GhostFoto multispectral imaging systembased on AggieAir architecture is proven to be an innovative and useful tool

AggieAir: An Integrated and Effective Small Multi-UAV Command, Control and Data Collection Architecture

Small UAV performance depends on an effective and efficient command system architecture. Based on an existing UAV system called Paparazzi, AggieAir is a full flight system capable of handling single or multiple UAVs with single or multiple payloads per airframe. System-level block diagrams are presented and specific details about implementation and results are provided

AggieAir: An integrated and effective small multi-UAV command, control and data collection architecture

Small UAV performance depends on an effective and efficient command system architecture. Based on an existing UAV system called Paparazzi, AggieAir is a full flight system capable of handling single or multiple UAVs with single or multiple payloads per airframe. System-level block diagrams are presented and specific details about implementation and results are provided

Programmable Multispectral Imager Development as Light Weight Payload for Low Cost Fixed Wing Unmanned Aerial Vehicles

In this paper, we have developed a light-weight and cost-efficient multispectral imager payload for low cost fixed wing UAVs (Unmanned Aerial Vehicles) that need no runway for take-off and landing. The imager is band-reconfigurable, covering both visual (RGB) and near infrared (NIR) spectrum. The num-ber of the RGB and NIR sensors is scalable, depending on the demands of specific applications. The UAV on-board microcom-puter programs and controls the imager system, synchronizing each camera individually to capture airborne imagery. It also bridges the payload to the UAV system by sending and receiv-ing message packages. The airborne imagery is time-stamped with the corresponding local and geodetic coordinates data mea-sured by the onboard IMU (Inertia Measurement Unit) and GPS (Global Positioning System) module. Subsequently, the imagery will be orthorectified with the recorded geo-referencing data. The application of such imager system includes multispectral re-mote sensing, ground mapping, target recognition, etc. In this paper, we will outline the technologies, demonstrate our experi-mental results from actual UAV flight missions, and compare the results with our previous imager system