Traditional logic optimization relies on proxy metrics to approximate post-mapping performance and area, which may not correlate well with post-mapping delay and area. This paper explore a ground-truth-based optimization flow that directly incorporates the post-mapping delay and area during optimization using decision tree-based machine learning models. Results show high prediction accuracy and generalization to unseen designs,

Synthetic aperture radar (SAR) is a high-resolution remote sensing technology widely employed for ground and sea surface target detection. However, due to the unique imaging mechanism and information representation of SAR images, conventional spatial-domain feature extraction methods often struggle to fully capture their discriminative features. To address this limitation, this letter introduces the wavelet domain as an additional feature extraction space and proposes a dual-domain feature-enhanced network based on wavelet attention for SAR ship detection. Specifically, two wavelet attention modules are designed to independently and jointly compute attention for high-frequency and low-frequency features in the wavelet domain. Meanwhile, an embedding grouping strategy is adopted to reduce computational costs while enhancing the model’s detailed perception and global understanding of ship targets. Furthermore, a dynamic domain fusion module is proposed to more effectively integrate wavelet-domain and spatial-domain information, enriching feature representation. Comprehensive experiments on two widely used SAR ship datasets demonstrate that the proposed method outperforms many other state-of-the-art detectors. The source code is available at https://github.com/Wenjing-Jiang-hbu/DFWA-Net.

Accurate dense depth prediction for 3-D reconstruction of monocular endoscopic images plays an essential role in expanding the surgical field in robotic surgery. However, it is generally a challenge to precisely estimate dense depth due to complex surgical fields with limited field of viewing, illumination variations, and variable texture structure. This work explores the performance of convolutional networks and transformer-based networks for endoscopic depth prediction, and proposes a new architecture called densely convolved transformer aggregation networks (DCTAN) that can aggregate local texture features and global spatial-temporal features for endoscopic dense depth recovery. Specifically, DCTAN creates a new hybrid encoder that combines dense convolution and scalable transformers to parallel extract local texture features and global spatial-temporal features from monocular endoscopic video sequences. Then, a local and global aggregation decoder is established to assemble the tokens of each frame to generate the global feature maps, that are integrated with the corresponding local feature maps to predict depth from coarse to fine. We trained and evaluated DCTAN through self-supervised learning on monocular synthesis (ground-truth) data and colonoscopic video images, with the experimental results demonstrating that our new architecture can extract more accurate local and global features for depth prediction and achieve more accurate depth range, more complete depth structure, and more sufficient texture information than other networks. In particular, all qualitative and quantitative assessment results of our method are better than current monocular dense depth estimation models.

Traditional electronic design automation (EDA) techniques struggle to fulfill the stringent efficiency and quick turnaround demands of complex integrated systems. Machine learning (ML) strategies for EDA (“ML EDA”) are pivotal in transforming EDA to address these challenges. However, they encounter significant obstacles due to inadequate infrastructure, ranging from datasets to software interfaces. This paper demonstrates a software infrastructure for ML EDA built on two key technologies: (i) OpenROAD’s Python APIs, and (ii) NVIDIA’s CircuitOps, an EDA data representation format tailored for ML, facilitating ML EDA applications. The paper illustrates three ML EDA examples that utilize the established OpenROAD and CircuitOps infrastructure.

Due to the unavailability of routing information in design stages prior to detailed routing (DR), the tasks of timing prediction and optimization pose major challenges. Inaccurate timing prediction wastes design effort, hurts circuit performance, and may lead to design failure. This work focuses on timing prediction after clock tree synthesis and placement legalization, which is the earliest opportunity to time and optimize a “complete” netlist. The article first documents that having “oracle knowledge” of the final post-DR parasitics enables post-global routing (GR) optimization to produce improved final timing outcomes. To bridge the gap between GR-based parasitic and timing estimation and post-DR results during post-GR optimization , machine learning (ML)-based models are proposed, including the use of features for macro blockages for accurate predictions for designs with macros. Based on a set of experimental evaluations, it is demonstrated that these models show higher accuracy than GR-based timing estimation. When used during post-GR optimization, the ML-based models show demonstrable improvements in post-DR circuit performance. The methodology is applied to two different tool flows—OpenROAD and a commercial tool flow—and results on an open-source 45nm bulk and a commercial 12nm FinFET enablement show improvements in post-DR timing slack metrics without increasing congestion. The models are demonstrated to be generalizable to designs generated under different clock period constraints and are robust to training data with small levels of noise.

This study presents a novel Supervised Information Enhanced Contrastive Learning Algorithm for Parkinson’s Disease Detection (SI-CLAPD) based on Electroencephalography (EEG). SI-CLAPD performs contrastive learning in a multi-granularity manner on enhanced contextual views to achieve robust contextual representations for EEG, thus could contribute to improving the effectiveness of PD detection. Unlike existing methods for constructing PD detection models guided solely by classification loss, we propose a joint learning model that combines self-supervised contrastive learning with supervised classification learning. This model is optimized using both contrastive loss and classification loss, allowing it to capture subtle differences between EEG signals and representations that are specific to PD detection. Through extensive experimental evaluations, we demonstrate that SI-CLAPD achieves robust and high accuracy in PD detection tasks on three benchmark datasets. To the best of our knowledge, this study represents the first effort in validating the effectiveness of contrastive learning for the detection of PD. Besides, within the realm of contrastive learning research in EEG, it also represents the first endeavor to fuse supervised learning with self-supervised contrastive learning for EEG classification. This investigation unveils an universally applicable approach to EEG signal processing, with the potential to confer advantages to a multitude of EEG classification tasks.

In this study, we propose a three-dimensional (3D) object recognition method using multifractal properties. The proposed method is based on a 3D multifractal detrended fluctuation analysis (MF-DFA) using the voxel data of an object. We propose a 3D MF-DFA by extending a conventional MF-DFA, which is widely adopted for analyzing the time series. In the current study, the data processed by the model were changed from the time-series data in the original MF-DFA into the voxels of 3D objects. Thus, the steps in the original model were extended to a 3D processing structure. To voxelize the scatter point data, we apply a modified Allen–Cahn (AC) equation with the Neumann boundary condition to generate the object volume. Various 3D models after voxelization are used for the 3D MF-DFA to calculate the generalized Hurst exponents . The calculated generalized Hurst exponents of different categories show different distributions and are used as the training feature input vector into a multi-classification system, i.e., one-versus-one support vector machines (OvO-SVMs). The computational results show that the proposed method can effectively extract the features of an object. Metrics such as the accuracy, precision, and recall are utilized to measure the efficiency and robustness. When compared with state-of-art algorithms, our empirical tests show that the proposed 3D MF-DFA-OvO-SVM system is superior to most of the methods in terms of classification accuracy . In addition, we also confirm that our proposed model is applicable to object detection in 3D space. An efficient method may be useful for autonomous driving, robot cruises, and AR-based intelligent user interfaces.

Abstract Cloud markets advocate powerful instances to take computation over from the cyber‐physical system (CPS). Combining the Cloud and CPS layer, the whole Cloud–CPS framework is designed to achieve both accurate data sensing and fast data analysis. While most researchers trust the computation side, and focus on the actuator in the physical space to ensure the service‐level objectives, SLO, that is, deadline misses, cloud can be a threat to the service sustainability as instance may fail, especially when one tries to make a cost‐effective design. Specifically, users must bear the risk of instance failure. These risks can cause the entire cyber‐physical system to collapse. Our work tackles the cloud aspect of the sustainability challenge from the cloud side in a cloud–CPS framework. We have studied the instance selection problem for the CPS systems, and propose a Cost‐Risk Analysis for Instance Recommendation, or CRAIR, to support a sustainable Cloud–CPS framework. We have adopted the classic risk analysis process from the portfolio management in hedge financial market, combining with the system modeling for the CPS instance selection, as an optimization problem. To solve this problem, we formulate it as a multi‐armed bandit problem and solve it with our upper confidence bound bandit algorithm together, our CRAIR can provide an online risk analysis to maximize the profit with a comparative ratio of O(1+ ). We have evaluated CRAIR based on simulations using real‐world Google and Alibaba workloads and cloud market numbers. The results show that, compared to traditional approaches, our approach provides the best tradeoff between SLOs and costs. All users achieve their SLOs goals while minimizing their average expenses by 34.6%. By using CRAIR for instance selection, the CPS service can maximize its benefit under a controlled risk.