Miracle-dz/UniFAR-FTUs huggingface dataset:
ReaDy: A ReRAM-Based Processing-in-Memory Accelerator for Dynamic Graph Convolutional Networks

Dynamic graph convolutional networks (DGCNs) have emerged as an effective approach to analyzing graph data that is constantly changing. The typical DGCNs incorporate not only graph convolutional networks (GCNs) to extract the structural information but also with recurrent neural networks (RNNs) to capture the temporal information from evolving graph data. These two alternative execution kernels of DGCNs impose unique architecture challenges for both types of kernels to be implemented efficiently. The presence of complex execution patterns of DGCNs renders existing architectures unsuitable. In this article, we present the first DGCN accelerator with an integrated architecture, named ReaDy, to accelerate DGCNs based on emerging PIM-featured ReRAM architectures. ReaDy is novel with an integrated architecture that enables running the GCN and RNN kernels of DGCNs simultaneously. Specifically, ReaDy is equipped with a redundancy-free scheduling mechanism to alleviate intrinsic dynamic irregularity for the GCN kernel, improving hardware utilization. In addition, ReaDy also includes a locality-aware dataflow strategy to exploit the inherent intervertex data locality for the RNN kernel, reducing superfluous data accesses to vertices and weight parameters. In a holistic view, ReaDy further enhances the entire system via an interkernel pipeline to reduce the off-chip accesses of intermediate results, boosting the overall efficiency of DGCNs significantly. Compared to the state-of-the-art software framework, PyGT, running on Intel Xeon E5-2680v4 CPU and NVIDIA Ampere A100 GPU, ReaDy achieves the average speedups of <inline-formula> <tex-math notation="LaTeX">$955\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$27.33\times $ </tex-math></inline-formula>, and the average energy savings of 1<inline-formula> <tex-math notation="LaTeX">$093\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$80.21\times $ </tex-math></inline-formula>, respectively. In addition, ReaDy outperforms ReFlip-ERA, which is obtained by combining a state-of-the-art GCN accelerator ReFlip and RNN accelerator ERA-LSTM, by an average speedup of <inline-formula> <tex-math notation="LaTeX">$8.30\times $ </tex-math></inline-formula> and an average energy saving of <inline-formula> <tex-math notation="LaTeX">$7.29\times $ </tex-math></inline-formula>.
Light neutral CP-even Higgs boson within Next-to-Minimal Supersymmetric Standard model (NMSSM) at the Large Hadron electron Collider (LHeC)

We analyze the prospects of observing the light CP-even neutral Higgs bosons ($h_1$) in their decays into $b \bar b$ quarks, in the neutral and charged current production processes $e h_1 q$ and $\nu h_1 q$ at the upcoming LHeC, with $\sqrt s \approx 1.296$ TeV. Assuming that the intermediate Higgs boson ($h_2$) is Standard Model (SM)-like, we study the Higgs production within the framework of NMSSM. We consider the constraints from Dark-matter, Sparticle masses, and the Higgs boson data. The signal in our analysis can be classified as three jets, with electron (missing energy)coming from the neutral (charged) current interaction. We demand that the number of b-tagged jets in the central rapidity region be greater or equal to two. The remaining jet is tagged in the forward regions. With this forward jet and two $b$-tagged jets in the central region, we reconstructed three jets invariant masses. Applying some lower limits on these invariant masses turns out to be an essential criterion to enhance the signal--to--background rates, with slightly different sets of kinematical selections in the two different channels. We consider almost all reducible and irreducible SM background processes. We find that the non-SM like Higgs boson, $h_1$, would be accessible in some of the NMSSM benchmark points, at approximately 0.4$\sigma$ (2.5$\sigma$) level in the $e$+3j channel up to Higgs boson masses of 75 GeV and in the $E\! \! \! \! /_T$+3j channel could be discovered with 1.7$\sigma$ (2.4$\sigma$) level up to Higgs boson masses of 88 GeV with 100 fb$^{-1}$ of data in a simple cut-based (with optimization) selection. With ten times more data accumulation at the end of the LHeC run and using optimization, one can have 5$\sigma$ discovery in the electron (missing energy) channel up to 85 (more than 90) GeV.
On the Limitations of PSO in Cooperation with FPI-based Adaptive Control for Nonlinear Systems

Essentially two kinds of adaptive controllers exist. To first-class belong those that commence their work with an available analytically known approximate system model and try to refine its parameters by using real-time observations. The elements of the second class do not wish to amend this initial model: rather they apply casual, not permanently maintained model corrections that rather depend on the observed situations that occur during the actual motion of the controlled system. Theoretically, it can be expected that an analytical model in the background can be refined, and placed in use even in this latter case. By the use of the van der Pol oscillator as a benchmark example of nonlinear systems and the Particle Swarm Optimization method for model correction, it is realized that our idea is too optimistic and the correction of the analytical model by learning may have limitations. This statement is substantiated via simulation examples.
Model Predictive Control for An Improved Transformer-less Five-level PV Inverter Topology

Power inverters have been located at the heart of photovoltaic (PV) applications. Transformer-less inverters possess several benefits over transformer-based PV inverters such as low volume, low cost, and higher effectiveness due to not use a transformer. This research introduces an improved 5-level transformerless inverter to apply in a single-phase grid-connected PV system. The proposed topology due to the use of a flying capacitor in its circuit can boost PV voltage like a DC-DC converter without using passive elements. To control the system a Finite Control Set-Model Predictive Control (FCS-MPC) is presented to evaluate the performance of the proposed inverter. The simulation results show that the presented FCS-MPC has a good performance during environmental disturbances in transient and steady-state operations in terms of grid current and PV current control as well as capacitor voltage control.
FastTransLog: A Log-based Anomaly Detection Method based on Fastformer

In daily operation, the log, as one of the most important information to record the status of the system, is a part of the content we need to pay attention to. Therefore, there are a lot of research on log anomaly detection. However, through our learning, it was found that these models based on log parsing obviously had the following shortcomings: 1) Easily affected by out-of-vocabulary words, accuracy of the result is decreased. And 2) taking a long time to calculate. In order to remedy the above defects, I propose a log exception detection method based on Fast-Former namely FastTransLog in this paper, and abandon the traditional log parsing process. Through this method, not only the algorithm speed is greatly improved, but also the accuracy of the datasets is up to 99%.
Analysis of the Relaxation Effect by Vibrations on Some Rheological Models of Tissues

Vibration therapy, or biomechanical stimulation, is a method used for improvement of muscle strength, power, and flexibility and has become a very popular method in recent years, both in physical therapy and in sports. This popularity is due to the fact that, as a result of analyzing the groups of subjects, the effects of small amplitude vibration and low frequency vibration, were found an increase in the force developed by the feet, a hardening of bone strength or an increase in bone density. The generic term used where any vibration of any frequency is transferred to the human body is “whole-body vibration”. In this paper it is proposed to give a possible explanation of the stress relaxation in muscle and/or bone after whole body vibration treatment. To do this, it is considered some rheological models that, when subjected to vibrations and analysis of their response, lead to the results obtained from different experiments.
Preplacement Net Length and Timing Estimation by Customized Graph Neural Network

Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow. However, the bulk of net length information is not available until cell placement, and hence, it is a significant challenge to explicitly consider net length optimization in design stages prior to placement, such as logic synthesis. In addition, the absence of net length information makes accurate preplacement timing estimation extremely difficult. Poor predictability on the timing not only affects timing optimizations but also hampers the accurate evaluation of synthesis solutions. This work addresses these challenges by a preplacement prediction flow with estimators on both net length and timing. We propose a graph attention network (GAT) method with customization, called Net2, to estimate individual net length before cell placement. Its accuracy-oriented version Net2a achieves about 15% better accuracy than several previous works in identifying both long nets and long critical paths. Its fast version Net2f is more than $1000\times $ faster than placement while still outperforms previous works and other neural network techniques in terms of various accuracy metrics. Based on net size estimations, we propose the first machine learning-based preplacement timing estimator. Compared with the preplacement timing report from commercial tools, it improves the correlation coefficient in arc delays by 0.08, and reduces the mean absolute error in slack, worst negative slack, and total negative slack estimations by more than 50%.
Black Start and Islanding Operation of Wind Turbines with Auxiliary Power Converters and Energy Storage Systems

The ability to black start a grid and operate it in islanded operation is an important feature in power generation units. For renewable energy sources, especially wind turbines, a unified black start strategy does not yet exist and different methods are possible. In general, this is done with a grid-forming control, which can be implemented in the wind turbine controller but also requires major modifications. This paper proposes a novel black start strategy which uses an auxiliary converter to black start the wind turbine and control a microgrid. The advantage of this method is that there is no need to change the overall wind turbine control and it can operate in the conventional grid-feeding or grid-supporting mode. The only changes needed can be done through the active and reactive power set-points and reference values. A grid-forming control with an auxiliary converter as well as three different ways of calculating suitable power set-points for the wind turbine are presented which allow for a stable microgrid control and accurate load-sharing of the two converters. In the end simulations are carried out and the developed methods are compared. The results show that this system setup offers multiple successful control strategies for the black start and grid control which can be used in future microgrids.
Tailoring Radar-Based Patient Monitoring Models to Real-Life Needs using Utility Maximization

Monitoring patients in hospitals or care homes using radars is an interesting problem with life-saving applications. Today deep neural networks are employed for patient monitoring, but these do not provide uncertainties, nor do they consider the asymmetry in the real life cost of misclassifying different activities. In this work we use Bayesian Neural Networks that provide uncertainty on their predictions. We combine these models with a self-defined utility function to obtain tailored predictions that are more conservative for classes where misclassifications come at a higher risk or cost. We show that Bayesian neural networks are more robust, and generalize better on radar human-activity images than deterministic ones, and that they are able to reduce the cost of misclassifications in a realistic example setting by 37% compared to approaches from literature.
Anchor Pair Selection in TDOA Positioning Systems by Door Transition Error Minimization

This paper presents an adaptive anchor pairs selection algorithm for UWB (ultra-wideband) TDOA-based (Time Difference of Arrival) indoor positioning systems. The method assumes dividing the system operation area into zones. The most favorable anchor pairs are selected by minimizing the positioning errors in doorways leading to these zones where possible users’ locations are limited to small, narrow areas. The sets are determined separately for going in and out of the zone to take users’ body shadowing into account. The determined anchor pairs are then used to calculate TDOA values and localize the user moving around the apartment with an Extended Kalman Filter based algorithm. The method was tested experimentally in a furnished apartment. The results have shown that the adaptive selection of the anchor pairs leads to an increase in the user’s localization accuracy. The median trajectory error was about 0.32 m.
CoopEdge: Cost-effective Server Deployment for Cooperative Multi-Access Edge Computing

The combination of 5G and edge computing has been envisioned as a promising paradigm to empower pervasive and intensive computing for the Internet-of-Things (IoT). High deployment cost is one of the major obstacles for realizing 5G edge computing. Most existing works tried to deploy the minimum number of edge servers to cover a target area by avoiding coverage overlaps. However, following this framework, the resource requirement per server will be drastically increased by the peak requirement during workload variations. Even worse, most resources will be left under-utilized for most of the time. To address this problem, we propose CoopEdge, a cost-effective server deployment scheme for cooperative multi-access edge computing. The key idea of CoopEdge is to allow deploying overlapped servers to handle variable requested workloads in a cooperative manner. In this way, the peak demands can be dispersed into multiple servers, and the resource requirement for each server could be greatly reduced. We further propose a two-step incremental algorithm, which jointly decides the server deployment and cooperation policies for fast convergence. We evaluate CoopEdge based on seven real-world edge applications. The results show that compared with the state-of-the-art works, CoopEdge significantly reduces the deployment cost by 38.7% and improves resource utilization by 36.2%.
Ka-Band Wide Gain Control Range CMOS Variable Gain Amplifier Using PMOS for 5G Communications

This paper presents the variable gain amplifier (VGA) implemented in a 65 nm RF CMOS process. It works in Ka-band for 5G Communication, with wide bandwidth, low RMS phase error, and wide gain control range performance. A shunt resistor was used to create a low Q inductor for wide bandwidth. In addition, PMOS and common-gate NMOS with opposite parasitic capacities were utilized to ensure low RMS phase error. We also optimize the size of the PMOS to achieve a wide gain control range. The core size of the designed chip was 0.056 mm2. The designed device shows a 16.0 dB gain control range from 0 to 16.0 dB. The measured 3-dB bandwidth is 6 GHz and the measured RMS phase error is lower than 2.67°.
An Architectural Charge Management Interface for Energy-Harvesting Systems

Energy-harvesting devices eliminate batteries, instead collecting their operating energy from environmental sources. A device stores energy into a capacitor, drawing energy to perform tasks and powering off to recharge when the energy is exhausted. State-of-the-art charge management systems for these devices aim to avoid power failure during task execution by reasoning about task energy cost. We identify that the innate equivalent series resistance (ESR) in energy storage capacitors breaks energy-based systems’ guarantees; running high current load on a high-ESR capacitor causes a substantial voltage drop that rebounds once the load is removed. This voltage drop is disregarded by systems that only reason about energy. If the drop lowers the voltage below the system’s operating threshold, however, the device powers off while stored energy remains. Though ESR is well understood in hardware design, this is the first work to argue that software for batteryless devices must also be aware of ESR.This work presents Culpeo, a hardware/software mechanism and architectural interface to relay the effect of ESR in the power system to software. We develop static and dynamic implementations of Culpeo and demonstrate on real batteryless devices that considering ESR restores correctness guarantees broken by energy-only charge management. We then demonstrate how to integrate Culpeo’s safe voltage into state-of-the-art schedulers, restoring task deadline guarantees for applications with predictable energy harvesting. Finally, we propose an on-chip Culpeo hardware implementation that allows for runtime monitoring of the effects of ESR to respond to changes in harvestable power.
Reactive Power Support from Converter Connected Renewables in Active Distribution Network

Network operators have been increasingly concerned with procuring reactive power sources as more synchronous generators are planned to be decommissioned and number of distributed generation is on the rise. Reactive power is necessary in the evolving grid with distributed generation to support the additional power flow and avoid expensive grid reinforcements. Converter connected renewable generators can be a solution both to provide the much needed reactive power support in distribution grids and the voltage regulation support to the transmission network via altering the reactive power feed-in. This research explores the reactive power capabilities of a mutli-voltage active distribution network. A semidefinite programming based optimal power flow is implemented to find the maximum reactive power support from converter connected generation having two different objectives, namely loss minimization in the distribution network and following a reactive power reference from the transmission network. Changes in voltage profiles in correlation to altering reactive power flow in the distribution network is also analyzed.
The topology of large-scale structure. III: Analysis of observations

A recently developed algorithm for quantitatively measuring the topology of large-scale structures in the universe was applied to a number of important observational data sets. The data sets included an Abell (1958) cluster sample out to Vmax = 22,600 km/sec, the Giovanelli and Haynes (1985) sample out to Vmax = 11,800 km/sec, the CfA sample out to Vmax = 5000 km/sec, the Thuan and Schneider (1988) dwarf sample out to Vmax = 3000 km/sec, and the Tully (1987) sample out to Vmax = 3000 km/sec. It was found that, when the topology is studied on smoothing scales significantly larger than the correlation length (i.e., smoothing length, lambda, not below 1200 km/sec), the topology is spongelike and is consistent with the standard model in which the structure seen today has grown from small fluctuations caused by random noise in the early universe. When the topology is studied on the scale of lambda of about 600 km/sec, a small shift is observed in the genus curve in the direction of a meatball topology. 66 refs.
Featherweight Soft Error Resilience for GPUs

This paper presents Flame, a hardware/software co-designed resilience scheme for protecting GPUs against soft errors. For low-cost yet high-performance resilience, Flame uses acoustic sensors and idempotent processing for error detection and recovery, respectively. That is, Flame seeks to correct any sensor-detected errors by re-executing the idempotent region where they occurred. To achieve this, it is essential for each idempotent region to ensure the absence of errors before moving on to the next region. This is so-called soft error verification that takes sensors’ worst-case detection latency (WCDL) to verify each region finished. Rather than waiting for WCDL at each region end, which incurs too much performance overhead, Flame proposes WCDL-aware warp scheduling that can hide the error verification delay (i.e., WCDL) with GPU’s inherent massive warp-level parallelism. When a warp hits each idempotent region boundary, Flame deschedules the warp and switches to one of the other ready warps—as if the region boundary were a regular long-latency operation triggering the warp switching. By leveraging GPU’s inherent ability for the latency hiding, Flame can completely eliminate the verification delay without significant hardware modification. The experimental results demonstrate that the performance overhead of Flame is near zero, i.e., 0.6% on average for 34 GPU benchmark applications.
Memory-Computing Decoupling: A DNN Multitasking Accelerator With Adaptive Data Arrangement

Multiple deep neural networks (DNNs) are increasingly used in real-world intelligent applications, such as intelligent robotics and autonomous vehicles to collectively complete complicated tasks running on edge devices. Because each layer of the subtasks prefers a distinct dataflow due to the heterogeneity in shape and scale of the network layers, a variable dataflow approach on the DNN accelerators is urgently required. On DNN accelerators that enable multiple dataflows, however, we detect a dimension mismatch between parallel processing under the dataflow approach and linear data memory arrangement. When multiple DNN tasks share partial features or weights, the issue is further exacerbated. During processing, this mismatch causes a sluggish data supply from both off-chip and on-chip memory. Consequently, the overall throughput, performance, and energy efficiency suffer since DNN models are sensitive to data density. In this work, we reveal the mechanism behind this data dimension mismatch and present a series of metrics that quantify the influence on system performance. On this foundation, we offer a framework that tracks the data tensor dimension conversion and employs a flexible data arrangement over multi-DNN computation to adapt to dataflow variability. An accelerator architecture named data arrangement multi-DNN accelerator (DARMA) that features a data arrangement and distribution circuit and hierarchical memory for data dimension conversion is also presented. Since the mismatch is mitigated, the suggested accelerator outperforms current accelerators in terms of bandwidth and processing unit utilization. Through tests on VR/AR, MLperf, and other multitask applications, the evaluation results show that the proposed architecture provides both energy-efficiency and throughput improvements.
Integrating rotationally-symmetric 2D-FEM into a dedicated rotationally-symmetric Mode-Matching for waveguide devices

There are many tools for the study of waveguide devices. The two-dimensional Finite Element Method is one of the most relevant ones when the objective is performing modal analysis, and Mode-Matching is widely used when analysing discontinuities in terms of modes. Due to the synergies between both methods, it is very interesting to study their combination in special cases commonly appearing in the literature, such as the problem of discrete rotationally-symmetric waveguides. In this work we present how the integration of the 2D Finite Element Method for Mode-Matching with rotationally symmetric modes must be done, so that it may employed to perform a more efficient and accurate analysis of devices composed of waveguide discontinuities.