Miracle-dz/UniFAR-FTUs huggingface dataset:
The ZuSE-KI-Mobil AI Accelerator SoC: Overview and a Functional Safety Perspective

ZuSE-KI-Mobil (ZuKIMo) is a nationally funded research project, currently in its intermediate stage. The goal of the ZuKIMo project is to develop a new System-on-Chip (SoC) platform and corresponding ecosystem to enable efficient Artificial Intelligence (AI) applications with specific requirements. With ZuKIMo, we specifically target applications from the mobility domain, i.e. autonomous vehicles and drones. The initial ecosystem is built by a consortium consisting of seven partners from German academia and industry. We develop the SoC platform and its ecosystem around a novel AI accelerator design. The customizable accelerator is conceived from scratch to fulfill the functional and non-functional requirements derived from the ambitious use cases. A tape-out in 22 nm FDX-technology is planned in 2023. Apart from the System-on-Chip hardware design itself, the ZuKIMo ecosystem has the objective of providing software tooling for easy deployment of new use cases and hardware-CNN co-design. Furthermore, AI accelerators in safety-critical applications like our mobility use cases, necessitate the fulfillment of safety requirements. Therefore, we investigate new design methodologies for fault analysis of Deep Neural Networks (DNNs) and introduce our new redundancy mechanism for AI accelerators.
Classification and Prediction of Incipient Faults in Transformer Oil by Supervised Machine Learning using Decision Tree

Transformer condition evaluation is a crucial technique for displaying a transformer’s health index. Key gases (KG), which dissolve in oil and become inseparable, are developed because of the different nascent flaws formed in the transformer enclosure. The exact defect that caused the gases to evolve may be found using the composition of KG present in the transformer. Five KG, including hydrogen H2, methane (CH4), ethane (C2 H2), ethylene C2 H4), and acetylene (C2 H2), are employed in this study as an input to anticipate the real incipient defect. In the inquiry that follows, classification is carried out using a Decision Tree (DT) and the data is trained using a Supervised Machine Learning (SML). The dataset is made up of 180 different input KG compositions and defects that are trained, identified, and predicted using a predictive model. 16 more datasets with KG composition are utilized for prediction. TPR versus FNR, PPV vs FDR, AUC, parallel coordinates plot, confusion matrix, and lowest classification error are used to assess the statistical correctness. The accuracy of the projected data using DT and optimized DT is 62.9%, however the accuracy of the optimized DT with principal component analysis (PCA) is only 55%. Therefore, the suggested DT fault classification approach may be utilized to accurately forecast the onset of faults.
PIMPR: PIM-based Personalized Recommendation with Heterogeneous Memory Hierarchy

Deep learning-based personalized recommendation models (DLRMs) are dominating AI tasks in data centers. The performance bottleneck of typical DLRMs mainly lies in the memory-bounded embedding layers. Resistive Random Access Memory (ReRAM)-based Processing-in-memory (PIM) architecture is a natural fit for DLRMs thanks to its in-situ computation and high computational density. However, it remains two challenges before DLRMs fully embrace ReRAM-based PIM architectures: 1) The size of DLRM's embedding tables can reach tens of GBs, far beyond the memory capacity of typical ReRAM chips. 2) The irregular sparsity conveyed in the embedding layers is difficult to exploit in ReRAM crossbars architecture. In this paper, we present a PIM-based DLRM accelerator named PIMPR. PIMPR has a heterogeneous memory hierarchy-ReRAM crossbar-based PIM modules serve as the computing caches with high computing parallelism, while DIMM modules are able to hold the entire embedding table-leveraging the data locality of DLRM's embedding layers. Moreover, we propose a runtime strategy to skip the useless calculation induced by the sparsity and an offline strategy to balance the workload of each ReRAM crossbar. Compared to the state-of-the-art DLRM accelerator SPACE and TRiM, PIMPR achieves on average 2.02×and 1.79× speedup, 5.6 ×, and 5.1 × energy reduction, respectively.
Davida: A Decentralization Approach to Localizing Transaction Sequences for Debugging Transactional Atomicity Violations

Atomicity is a desirable property for multithreaded programs. In such programs, a transaction is an execution of an atomic code region that may contain memory accesses on an arbitrary number of shared variables. When transactions are not conflicting with one another in a trace, they greatly simplify the reasoning of the program correctness. If a transaction incurs an atomicity violation in a trace, developers have to debug the code, but this is challenging. To achieve practical runtime performances, existing dynamic techniques for detecting such atomicity violations face a challenge: They are designed for either detecting all such atomicity violations without the capability of localizing the corresponding cross-thread transaction sequences or deliberately missing some atomicity violations in the trade of localizing some of them to support their atomicity violation detection. In this article, we propose Davida, a novel technique to address this problem. Davida efficiently tracks selective transactions and cross-thread dependency sequences over transactions reachable from the currently active transactions of all the threads in a decentralized manner. We prove that Davida precisely accomplishes every atomicity violation in a trace with an actual sequence of transactions triggering the violation. The experimental results on 15 subjects showed that Davida outperformed Velodrome, the previous graph-based state-of-the-art technique, in both performance and completeness.
Layer-Puzzle: Allocating and Scheduling Multi-task on Multi-core NPUs by Using Layer Heterogeneity

In this work, we propose Layer-Puzzle, a multi-task allocation and scheduling framework for multi-core NPUs. Based on the proposed latency-prediction model and dynamic parallelization scheme, Layer-Puzzle can generate near-optimal results for each layer under given hardware resources and traffic congestion levels. As an online scheduler, Layer-Puzzle performs a QoS-aware and dynamic scheduling method that picks the superior version from the previously compiled results and co-runs the selected tasks to improve system performance. Our experiments on MLPerf show that Layer-Puzzle can achieve up to 1.61X, 1.53X, and 1.95X improvement in ANTT, STP, and PE utilization, respectively.
Factors Influencing Users to Use Chatbots in Banking and Fintech Industry in Indonesia

Chatbots are growing in Indonesia's banking and fintech industries. Not only answer frequently asked questions, but chatbots are also becoming virtual assistants. For example, BCA and Bank Mandiri provide chatbots with the ability to check personal credit information. KPR Academy, a fintech for residential mortgages, provides chatbots to help with loan applications. But besides its benefits, Indonesia is the third world-ranking country with the highest number of data leak cases. The suggested model was developed to examine significant factors for determining a user's behavioral intention to use chatbots in the current situation. Respondents came from 442 Indonesian banking and fintech customers validated using PLS-SEM. The best predictors of a user's behavioral intention when using a chatbot are perceived trust, performance expectations, and social influence. Perceived risk, effort expectation, and self-efficacy are not significant factors. This study concludes an understanding of factors determining chatbot adoption in Indonesia and input for banks and fintech industries in developing chatbots.
Ditty: Directory-based Cache Coherence for Multicore Safety-critical Systems

Ditty is a predictable directory-based cache co-herence mechanism for multicore safety-critical systems that guarantees a worst-case latency (WCL) on data accesses. Prior approaches for predictable cache coherence use a shared snooping bus to interconnect cores. This restricts the number of cores in the multicore to typically four or eight due to scalability concerns. Ditty takes a first step towards a scalable cache coherence mechanism that is predictable and one that can support a larger number of cores. In designing Ditty, we propose a coherence protocol and micro-architecture additions to deliver a WCL bound that is lower than a naive approach. Our WCL analysis reveals that the resulting bounds are comparable to state-of-the-art bus-based predictable coherence approaches. We prototype Ditty in hardware and empirically evaluate it on an FPGA. Our evaluation shows the observed WCL is within computed WCL bound for both the synthetic and SPLASH-3 benchmarks. We release our implementation to the public domain.
RECC: A Relationship-Enhanced Content Caching Algorithm Using Deep Reinforcement Learning

Mobile edge caching (MEC) is a promising technology to alleviate traffic congestion in the network. Current studies explored deep reinforcement learning (DRL)-based MEC methods. These methods consider the dynamics of the request size to maximize the cache hit rate. However, they usually ignored the potential request relationships among contents. Two contents with a strong relationship are usually requested sequentially. Inspired by this assumption, this paper proposes a relationship-enhanced content caching algorithm using DRL, named RECC. Our RECC infers user preferences by mining the request relationships among contents. In this work, the relationships are modeled as request sequences, and the request features are learned by using graph embedding. These features will be used as input of state in our DRL-based algorithm. We utilize the Wolpertinger architecture to solve the limitation of large discrete action space. The simulation results indicate that our RECC outperformed the traditional cache policies and state-of-the-art DRL-based method in cache hit rate. Furthermore, the proposed RECC has advantages in long-term stability in the environment where content popularity changes dynamically, and also has a higher cache hit rate when handling the requests with number changes dynamically.
Perspector: Benchmarking Benchmark Suites

Estimating the quality of a benchmark suite is a non-trivial task. A poorly selected or improperly configured bench-mark suite can present a distorted picture of the performance of the evaluated framework. With computing venturing into new domains, the total number of benchmark suites available is increasing by the day. Researchers must evaluate these suites quickly and decisively for their effectiveness. We present Perspector, a novel tool to quantify the performance of a benchmark suite. Perspector comprises novel metrics to characterize the quality of a benchmark suite. It provides a math-ematical framework for capturing some qualitative suggestions and observations made in prior work. The metrics are generic and domain-agnostic. Furthermore, our tool can be used to compare the efficacy of one suite vis-a-vis other benchmark suites, systematically and rigorously create a suite of workloads, and appropriately tune them for a target system.
Routability Prediction using Deep Hierarchical Classification and Regression

Routability prediction can forecast the locations where design rule violations occur without routing and thus can speed up the design iterations by skipping the time-consuming routing tasks. This paper investigated (i) how to predict the routability on a continuous value and (ii) how to improve the prediction accuracy for the minority samples. We propose a deep hierarchical classification and regression (HCR) model that can detect hotspots with the number of violations. The hierarchical inference flow can prevent the model from overfitting to the majority samples in imbalanced data. In addition, we introduce a training method for the proposed HCR model that uses Bayesian optimization to find the ideal modeling parameters quickly and incorporates transfer learning for the regression model. We achieved an R2 score of 0.71 for the regression and increased the Fl score in the binary classification by 94% compared to previous work [6].
Development and elaboration of numerical method for simulating gas–liquid–solid three-phase flows based on particle method

ABSTRACT A numerical method for simulating gas–liquid–solid three-phase flows based on the moving particle semi-implicit (MPS) approach was developed in this study. Computational instability often occurs in multiphase flow simulations if the deformations of the free surfaces between different phases are large, among other reasons. To avoid this instability, this paper proposes an improved coupling procedure between different phases in which the physical quantities of particles in different phases are calculated independently. We performed numerical tests on two illustrative problems: a dam-break problem and a solid-sphere impingement problem. The former problem is a gas–liquid two-phase problem, and the latter is a gas–liquid–solid three-phase problem. The computational results agree reasonably well with the experimental results. Thus, we confirmed that the proposed MPS method reproduces the interaction between different phases without inducing numerical instability.
A Latency, Throughput, and Programmability Perspective of GrPPI for Streaming on Multi-cores

Several solutions aim to simplify the burdening task of parallel programming. The GrPPI library is one of them. It allows users to implement parallel code for multiple backends through a unified, abstract, and generic layer while promising minimal overhead on performance. An outspread evaluation of GrPPI regarding stream parallelism with representative metrics for this domain, such as throughput and latency, was not yet done. In this work, we evaluate GrPPI focused on stream processing. We evaluate performance, memory usage, and programming effort and compare them against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is competitive with handwritten code in some cases, in other cases, the infeasibility of fine-tuning GrPPI is a crucial drawback. Despite this, programmability experiments estimate that GrPPI has the potential to reduce by about three times the development time of parallel applications.
ESSNet: Lightweight Semantic Segmentation Network

Semantic segmentation is widely used in fields such as self-driving, robot navigation and augmented virtual reality, which has extremely high requirements for the real-time reasoning and accurate segmentation of the network. Recently, semantic segmentation has made great progress in segmentation accuracy, but little attention has been paid to real-time reasoning. Therefore, it makes sense to propose a network to balance accuracy and real-time reasoning. In this paper, we propose a lightweight semantic segmentation network with Multiple Correction Confusion Mechanism (MCCM) and Lightweight Attention Mechanisms (LAM), named Lightweight Semantic Segmentation Network (ESSNet). MCCM selectively incorporates low-level feature to make the network segmentation contour clearer. LAM effectively captures the context information, making the feature representation more abundant. In general, different scale features confusion, and efficient attention mechanisms are adopted by LCANet to significantly improve its performance. To demonstrate the effectiveness of the network, we conducted experiments on the Cityscapes and CamVid datasets, and obtained MIOU of 68.7% and 74.8%, respectively, which is comparable to the state-of-the-art methods under the same experimental settings. In addition, on a single Titan RTX card, the network reached a forward reasoning speed of 136.4 FPS (480x480x3 image resolution).
Radiological effects after inhalation of highly radioactive fuel particles produced by the Chernobyl accident

A short review is presented of the results of long-standing research into the hot particles released into the environment after the Chemobyl accident, and their radiobiological significance. Due to the lack of direct measurements of hot particles in the lung, a conceptual method has been developed to estimate integral lung intake from measurements of soil contamination. The significance of the beta component of the radiation from hot particles has been reconsidered because this component may give rise to preconditions for oncogenic effects. A modification factor F has been introduced, which is defined as the ratio of average risks for different conditions of irradiation. It is shown that linear dose-effect models give the same results for uniform and point irradiation. Over the dose range 0.1 to 2 Gy it was found that a hot particle gives a higher risk of oncogenic detriment than the corresponding uniform dose distribution (F > 1). The corresponding modification factor shows a maximum value of 5 at -0.6 Gy.
Correlates on Job Performance, Job Satisfaction, and Demographics of State University Employees

This survey looked into the significant relationship between employees’ job performance and satisfaction levels, and the demographic profile of the employees of Bohol Island State University in Bohol, Philippines. Towards this end, using the descriptive-survey method of research with a quantitative-correlational approach, and the universal sampling technique, it surveyed 345 permanent employees. Specifically, the study identified the respondents’ age, sex, educational attainment, and length of service in the institution; the relationship between their demographics and their level of job performance, and the extent of job satisfaction. Findings revealed that the respondents were in their young adulthood stage, dominated by females, experienced employees in the institution, and had high educational credentials. Moreover, they performed well in their duties and were somewhat satisfied with their jobs. Despite the insignificant results of almost all of the null hypotheses tested, the respondents considered their length of service as a contributory factor that would influence their level of job performance and extent of job satisfaction, which implies that the longer their work experience, the better their work performance would be expected from them, and the more satisfied they are with their jobs. It indicates, therefore, that high levels of job satisfaction did not necessarily lead to employee performance. There may be other must-be studied factors that influenced one’s job performance and satisfaction.
Mathematical Modelling On Particle DiffusionIn Fluidised Beds And Dense TurbulentTwo-phase Flows

Volume-fraction weighted and Reynolds averaged momentum transport equations are solved in an Euler/Euler approach to numerically simulate the turbulent, dispersed two-phase flow in a two-dimensional channel and a three-dimensional conic diffuser flow. Particular attention is given to the modelling of turbulent diffusion and particle wall interaction, assuming local equilibrium, but introducing individual terms for particle/fluid drag interaction, particle collisions and trajectory crossings. These influences have been quantified in terms of partial viscosities, a restitution power and a turbulence structure parameter. Boussinesq approximations have been used for each phase and the formulation of their interaction was provided in the framework of the eddy-viscosity modelling concept.
Realised volatility prediction of high-frequency data with jumps based on machine learning

Asset price jumps are very common in financial markets, and they are essential to accurately predict volatility. This article focuses on 50 randomly selected stocks from the Chinese stock market, utilising high-frequency data to construct two jump models, the heterogeneous autoregressive quarticity jump model (HARQ-J) and the full heterogeneous autoregressive quarticity jump model (HARQ-F-J), which take into account jump variables based on existing models (HARQ and HARQ-F). To further enhance the accuracy of our volatility forecasts, the study combines the newly constructed models with the machine learning (ML) to form a hybrid model. Finally, the empirical research shows that the new hybrid model performs better than existing traditional prediction methods. In particular, the long- and short-term memory (LSTM) function is significantly better than other machine learning functions. Among all the LSTM models tested by the model confidence set (MCS), the HARQ-F-J-LSTM model has the highest prediction accuracy, followed by the HARQ-J-LSTM model.
Evaluating the Effectiveness of Calcium Silicate in Enhancing Soybean Growth and Yield

The application of silicon (Si) fertilizer positively impacts crop health, yield, and seed quality worldwide. Si is a “quasi-essential” element that is crucial for plant nutrition and stress response but is less associated with growth. This study aimed to investigate the effect of Si on the yield of cultivated soybean (Glycine max L). Two locations, Gyeongsan and Gunwi, in the Republic of Korea were selected, and a land suitability analysis was performed using QGIS version 3.28. 1. The experiments at both locations consisted of three treatments: the control, Si fertilizer application at 2.3 kg per plot (9 m × 9 m) (T1), and Si fertilizer application at 4.6 kg per plot (9 m × 9 m) (T2). The agronomic, root, and yield traits, as well as vegetative indices, were analyzed to evaluate the overall impact of Si. The results demonstrated that Si had consistently significant effects on most root and shoot parameters in the two experimental fields, which led to significantly increased crop yield when compared with the control, with T2 (22.8% and 25.6%, representing an output of 2.19 and 2.24 t ha−1 at Gyeongsan and Gunwi, respectively) showing a higher yield than T1 (11% and 14.2%, representing 1.98 and 2.04 t ha−1 at Gyeongsan and Gunwi, respectively). These results demonstrate the positive impact of exogenous Si application on the overall growth, morphological and physiological traits, and yield output of soybeans. However, the application of the optimal concentration of Si according to the crop requirement, soil status, and environmental conditions requires further studies.