Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Behavioural RTL Simulation of 10B Transistor SoC Designs with Metro-Mpi

Chips with tens of billions of transistors have become today's norm. These designs are straining our electronic design automation tools throughout the design process, requiring ever more computational resources. In many tools, parallelisation has improved both latency and throughput for the designer's benefit. However, tools largely remain restricted to a single machine and in the case of RTL simulation, we believe that this leaves much potential performance on the table. We introduce Metro-MPI to improve RTL simulation for modern 10 billion transistor-scale chips. Metro-MPI exploits the natural boundaries present in chip designs to partition RTL simulations and leverage High Performance Computing (HPC) techniques to extract parallelism. For chip designs that scale in size by exploiting latency-insensitive interfaces like networks-on-chip and AXI, Metro-MPI offers a new paradigm for RTL simulation scalability. Our implementation of Metro-MPI in Open-Piton+Ariane delivers 2.7 MIPS of RTL simulation throughput for the first time on a design with more than 10 billion transistors and 1,024 Linux-capable cores, opening new avenues for distributed RTL simulation of emerging system-on-chip designs. Compared to sequential and multithreaded RTL simulations of smaller designs, Metro-MPI achieves up to $135.98\times$ and $9.29\times$ speedups. Similarly, for a representative regression run, Metro-Mpireduces energy consumption by up to $2.53\times$ and $2.91\times$.
Temperature-Aware Sizing of Multi-Chip Module Accelerators for Multi-DNN Workloads

This paper demonstrates the need for temperature awareness in sizing accelerators to target multi-DNN workloads. To that end, we build TESA, a TEmperature-aware methodology that Sizes and places Accelerators to balance both the cost and power of a multi-chip module (MCM), including DRAM power for multi-deep neural network workloads. TESA tunes the accelerator chiplet size and inter-chiplet spacing to generate a temperature-aware MCM layout, subject to user-defined latency, area, power, and thermal constraints. Using TESA for both 2D and 3D systolic array-based chiplets, we demonstrate up to 44% MCM cost savings and 63% DRAM power savings, respectively, over a temperature-unaware baseline at iso-frequency and iso-interposer area. We also demonstrate a need for TESA to obtain feasible MCM configurations for multi-DNN workloads such as augmented/virtual reality (AR/VR).
Residential Electricity Power Forecasting using GRU-based CNN and SAE Ensembler

Smart grids play an important role to resolves issues related to electricity. Electrical load forecasting can be performed through smart grids to acquire knowledge about the electrical load that will be needed in the future. A combination of Stacked Autoencoding (SAE) and Gated Recurrent Unit (GRU) and a combination of Convolutional Neural Network (CNN), Grey Wolf Optimization (GWO), and GRU are proposed in this paper. Firstly, the historical load data is preprocessed and then preprocessed data is given to the feature selection and extraction methods to measure the importance of each feature. Features that have highest values are selected and input to the proposed classifiers. According to the results the proposed CNN-GRU-GWO gave the highest accuracy rate of 96.31%. This application of this article will help the utility purchase the exact amount of electricity from the grid, which will lead to grid stability.
Disturbance Observation Oriented Novel Sliding Mode Control of Buck-Boost Converter

In the traditional DC-DC converter circuit, some commonly used control methods have some problems in the control performance. With the development of modern control theory and the continuous improvement of implementation methods, many new control methods have emerged in the study of DC-DC converter control. Hence, this research study proposed the disturbance observation oriented novel sliding mode control of Buck-Boost converter. The basic idea of this research work is the sliding mode variable structure control, which is a nonlinear control method with the advantages of fast dynamic response, insensitivity to parameters and external disturbances, strong robustness, etc. Through the comparison test of output disturbance, it can be seen that the Super-Twisting sliding mode algorithm has strong robustness in the case of sudden increase, hence, this is considered as the innovation of the designed model. In order to reduce the switching frequency of the system, a hysteresis link is introduced in the controller. Simulation results show that the proposed sliding mode variable structure controller has fast response and strong robustness.
Malware Detection in Mobile Phones

Mobile devices today manage an increasing amount of sensitive data and are furnished with a number of capabilities and services. The amount of vulnerabilities that can be used to compromise mobile devices is likewise rising in line with this trend, and major mobile platforms like Android and iOS are clearly targets and have become more popular and widely used platforms these days. Which leads them to undergo vulnerable to various malware and hacks. As mobile malware advances, more attacks are leveraging these abrasive features. To keep information secure, an effective detecting technique is needed Future malware capability cannot always be kept up with by current methods for mobile malware analysis and detection. In this study, a detailed and organized summary of the most recent research on mobile malware detection methods, highlighting both their advantages and drawbacks.
Automated segmentation of lumen and media-adventitia in intravascular ultrasound images based on deep learning

Automated and accurate segmentation of lumen and media-adventitia in intravascular ultrasound images is essential for cardiologists to diagnose the coronary heart disease. In order to maximize the use of local information and global information, this paper proposes a lumen and media-adventitia segmentation model based on fusion of Convolutional Neural Network and Transformer. In addition, a channel enhancement module based on gated mechanism is constructed to enhance the feature map channels with more effective information, so as to improve the segmentation accuracy. Experiments show that the proposed segmentation model has excellent performance in multiple evaluation metrics, both for the lumen and for the media-adventitia of the vessel. The Jaccard Measure and Hausdorff distance for lumen segmentation are 91.9% and 0.201 mm, and 92.7% and 0.259 mm, respectively, for the media-adventitia segmentation. And the pixel accuracy rate is 98.49%. All these experimental results show that the proposed method could accurately segment the lumen and media-adventitia in intravascular ultrasound images.
Profile of CT scan output dose in axial and helical modes using convolution

The profile of the CT scan output dose is crucial for establishing the patient dose profile. The purpose of this study is to investigate the profile of the CT scan output dose in both axial and helical modes using convolution. A single scan output dose profile (SSDP) in the center of a head phantom was measured using a solid-state detector. The multiple scan output dose profile (MSDP) in the axial mode was calculated using convolution between SSDP and delta function, whereas for the helical mode MSDP was calculated using convolution between SSDP and the rectangular function. MSDPs were calculated for a number of scans (5, 10, 15, 20 and 25). The multiple scan average dose (MSAD) for differing numbers of scans was compared to the value of CT dose index (CTDI). Finally, the edge values of MSDP for every scan number were compared to the corresponding MSAD values. MSDPs were successfully generated by using convolution between a SSDP and the appropriate function. We found that CTDI only accurately estimates MSAD when the number of scans was more than 10. We also found that the edge values of the profiles were 42% to 93% lower than that the corresponding MSADs.
Search Recommendation Model Based on Attention Compressed Interaction Network

With the development of science and technology, recommender systems continue to attract the attention of countless fields, which provides personalized recommendations for users. In order to obtain more information in the review text and further improve the recommendation accuracy, this paper proposes a search recommendation model based on attention compressed interaction network. It combines the compressed interaction network and attention mechanism to capture the feature information of users and items from the review text, and then learns to predict the interaction between users and items, and based on the learned item representation. Mapping is performed in the natural language space to enable retrieval. Experimental results on the Amazon public dataset show that compared with other baseline models, the MAE and RMSE indicators can be reduced by up to 2.63%and 2.26%respectively, which demonstrates the feasibility and accuracy of the proposed model.
Delay-dependent absolute stability of nonlinear Lurie control systems with multiple time-delays

The stability of Lurie type control systems with multiple time-delays and nonlinearities is considered.Based on the Lyapunov stability theory,using an improved new integral inequality and the method of linear matrix inequality,a refined method called the integral inequality approach is presented.Neither model transformation nor bounding technique for cross terms is involved,and a sufficient condition for the absolute stability of Lurie system is derived and expressed in the form of linear matrix inequality.The criterion is also extended to systems with uncertainties and indirection.Examples are given to illustrate that the proposed criteria are less conservative than other existing ones.
PCB Design and Development of FEMTO-Satellite – Tanuk- 1

A cheap instrument for conducting scientific research tasks and testing new space technology is a nanosatellite mission. There were 371 nanosatellite launches between2000 and 2016, with 15.4% of them successfully completing their missions, and another 22.6% doing so in part. Numerous issues, including system instability, can be blamed for the high failure rate of nanosatellites. Integration, hardware not suitable for space, a failed launch, and unproven technologies. A some of the dangers By embracing design exploration, optimization, and testing, the risks and costs as well as pre-testing methods into system engineering methods. In this paper, a multidisciplinary approach to system discovery and design for low-cost Obtaining a more accurate assessment of system performance before starting the actual build. Since each system’s model is still in its early stages of development, it should be used with caution.
Silent Data Errors: Sources, Detection, and Modeling

Chip manufacturers and hyperscalers are becoming increasingly aware of the problem posed by Silent Data Errors (SDE) and are taking steps to address it. Major computing facilities operators like Meta and Google have emphasized the critical role of SDEs in today’s microprocessors. Numerous studies in the literature have highlighted the severity of this issue, especially in datacenter applications operating at large scales. These errors can lead to data loss and require a significant amount of time and effort to resolve through debugging engineering efforts, which can take months to complete. In this paper, we provide an overview of the issue of SDEs, including an explanation of the problem and the current methods used to address it, as well as gaps that still exist in addressing the issue. We also discuss the different sources of SDEs, including post-manufacturing testing failures, voltage and timing marginalities, and hard-to-detect faults. The paper emphasizes the impact of timing marginalities as a significant source of SDEs. Finally, our spotlight points to the architecture and system dimensions of the problem: we describe the challenges of measuring the true (still unknown) rates of SDE from CPUs, and emphasize on the role of detailed microarchitectural simulation models for this purpose. We present data on the severity of SDEs and their predicted rates under various operating conditions, sources of faults, and technology fabrication nodes.
Electrical Rule Checking of Integrated Circuits using Satisfiability Modulo Theory

We consider the verification of electrical properties of circuits to identify potential violations of electrical design rules, also called Electrical Rule Checking (ERC). We present a general approach based on Satisfiability Modulo Theory (SMT) to verify that these errors cannot occur in a given circuit. We claim that our approach is scalable and more precise than existing analyses, like voltage propagation. We applied these techniques to a specific type of errors, the missing level shifters. On an industrial case-study, our technique is able to flag 31 % of the warnings raised by the voltage propagation analysis as being false alarms.
Fast Detection and Tracking of Worn Lane Markings

A fast method for lanes detection is proposed to deal with worn lane markings. Lanes are detected based on regional pixels rather than edge points in this paper by concerning that the worn or faded lane markings lead to disruption to edge extraction. After pixels have voted, the local maximum is searched and a peak region is defined in the Hough space. The voting of a column in the peak region is considered as a stochastic variable, and the statistical characteristics are computed. The statistical variances are used to fit a quadratic function. The direction parameter of the lane marking is determined by the minimization of the fitted quadratic function. The statistical means are used to fit a linear function. The position parameter of the lane marking is computed using the interpolation technique. Lane tracking is implemented with lower computation cost by defining the peak region based on the lane parameters detected in previous frame. The experimental results show that the proposed method can detect effectively the lane markings even in presence of seriously worn roads. The computation time is less than 2ms for a road image.
Learning activities in online EFL classrooms through the lens of learning theories

Despite the fact that online learning, especially after the COVID-19 pandemic, has become the cornerstone of instruction all over the world, research on online learning activities has received little attention. To bridge the gap, this research investigated learning activities in online classrooms against the tenets of the prevalent learning theories, namely, behaviorism, cognitivism, constructivism, humanism, and connectivism. To meet the purpose of the study, a learning theory-based observation checklist was developed and used to observe 18 classes from two language schools in Guilan, Iran. In addition, semi-structured interviews were conducted to identify the extent to which teachers are influenced by learning theories in the selection of learning activities. The findings revealed that two learning theories, behaviorism and cognitivism, guided majority of the online EFL classrooms, with the former ruling the children's class and the latter governing the adult classes, respectively. However, activities connected to constructivism and humanism were occasionally observed, and Connectivism has totally been neglected. Considering this evidence, it can be inferred that online classes need to be revisited in light of learning theories, with teacher education as the first step.
Positioning Method of Intelligent Vehicle Based on Visual Multi-Feature Primitive Fusion

The robustness of visual localization algorithms is relatively poor when only visual point features are applied. Since the line feature is not sensitive to beam transformation, one description and extraction methodology based on long line features is proposed, in case that line features with length variations are obvious. ORB-SLAM2 framework is employed to fuse the point and line features, in order to complete the pose estimation and local optimization algorithm. Within the loopback detection module of the localization, spatial consistency and multipath fusion based on multi-features are applied to improve the recall rate of loop detection. Besides, path integration with correct re-position and camera pose transformation with multi-segment paths are successfully achieved. The real vehicle tests show that the point-line fusion algorithm has better robustness than single point localization algorithm. Also, the multi-path fusion algorithm contains higher mileage rate during the public dataset tests. The check result reaches 99.91% which is 10.71% higher than the traditional algorithm.
TOFU: A Two-Step Floorplan Refinement Framework for Whitespace Reduction

Floorplanning, as an early step in physical design, will greatly affect the PPA of the later stages. To achieve better performance while main-taining relatively the same chip size, the utilization of the generated floorplan needs to be high and constraints related to design rules, routability, power should be honored. In this paper, we propose a two-step framework, called TOFU, for floorplan whitespace reduction with fixed-outline and soft/pre- placed/hard modules modeled. Whitespace is first reduced by iteratively refining the locations of modules. Then the modules near whitespace will be changed into rectilinear shapes to further improve the utilization. To ensure the legality and quality of the intermediate floorplan during the refinement process, a constraint graph-based legalizer with a novel constraint graph construction method is proposed. Experimental results show that the whitespace of the initial floorplans generated by Corblivar [1] can be reduced by about 70% on average and up to 90% in several cases. Moreover, the resulting wirelength is also 3% shorter due to a higher utilization.
Research on test method of point cloud registration based on joint replacement

In a surgical navigation system, point cloud registration technology determines the accuracy of surgical navigation. Intelligent analysis of artificial joints to achieve higher quality measurement has become an urgent problem in point cloud registration. Based on deep learning, the experimental design and registration test method of point cloud acquisition based on artificial joint replacement are proposed. Experimental results show that the new algorithm can improve surgical accuracy. (Abstract)
Standalone Area Optimized ASIC Tag Powered and Programmable by Light for Identification of Novel Drug Candidates

We present a compact optical programmable ASIC-based tag for the identification of novel drug candidates, requiring no external components. Our tag aims for a time and cost-efficient solution to keep track of compounds during the split and pool synthesis. The ASIC is self-powered by integrated solar cells and optimized for intensive light in the range of 10Mlx. This intensive illumination can cause unwanted leakage currents in p-n junctions. Therefore, extensive care was taken to provide shielding for sensitive parts of the circuit and measurements prove the effectiveness. The ASIC is manufactured in a 0. 6µm process including special devices like EEPROM and photodiodes. Our tag contains an oscillator, optical receiver, reference voltage, and a digital controller. A custom optical communication protocol provides an energy-efficient data link to the smart tag with continuous power transfer.
PetaOps/W edge-AI $\mu$ Processors: Myth or reality?

With the rise of deep learning (DL), our world braces for artificial intelligence (AI) in every edge device, creating an urgent need for edge-AI SoCs. This SoC hardware needs to support high throughput, reliable and secure AI processing at ultra-low power (ULP), with a very short time to market. With its strong legacy in edge solutions and open processing platforms, the EU is well-positioned to become a leader in this SoC market. However, this requires AI edge processing to become at least 100 times more energy-efficient, while offering sufficient flexibility and scalability to deal with AI as a fast-moving target. Since the design space of these complex SoCs is huge, advanced tooling is needed to make their design tractable. The CONVOLVE project (currently in Inital stage) addresses these roadblocks. It takes a holistic approach with innovations at all levels of the design hierarchy. Starting with an overview of SOTA DL processing support and our project methodology, this paper presents 8 important design choices largely impacting the energy efficiency and flexibility of DL hardware. Finding good solutions is key to making smart-edge computing a reality.
Thermal Management for S-NUCA Many-Cores via Synchronous Thread Rotations

On-chip thermal management is quintessential to a thermally safe operation of a many-core processor. The presence of a physically distributed logically shared Last-Level Cache (LLC) significantly reduces the performance penalty of migrating threads within the cores of an S-NUCA many-core. This cost reduction allows novel thermal management of these many-cores via synchronous thread migration. Synchronous thread migration provides a viable alternative to Dynamic Voltage and Frequency Scaling (DVFS) and asynchronous thread migration used traditionally to manage thermals of S-NUCA many-cores. We present a theoretical method to compute the peak tem-perature in many-cores with synchronous thread migrations. We use the method to create a thermal management heuristic called HotPotato that maximizes the performance of S-NUCA many-cores under a peak temperature constraint. We implement HotPotato within the state-of-the-art HotSniper simulator. Detailed interval thermal simulations with HotSniper show an average 10.72% improvement in response time of S-NUCA many-cores when scheduling with HotPotato compared to a state-of-the-art thermal-aware S-NUCA scheduler.
Two-stage robust stochastic optimal scheduling method for multi-virtual power plant considering source-load side uncertainties

A two-stage robust stochastic optimal scheduling method is proposed for a virtual power plant with multiple energy sources in consideration of the uncertainties of the outside environment and the load and source sides. Firstly, the radius uncertainty set of adjustable robust coefficients is adopted to describe the output of renewable energy. And a two-stage robust stochastic optimization model is built. Secondly, the column constraint generation algorithm is adopted to decompose the original issues into major categories and subcategories. Thirdly, the dual transformation theory and the large M method is applied to transform and solve it iteratively. Finally, simulation analysis is used to substantiate the effectiveness of the proposed model and algorithm.