Miracle-dz/UniFAR-FTUs huggingface dataset:
DIMM-Link: Enabling Efficient Inter-DIMM Communication for Near-Memory Processing

DIMM-based near-memory processing architectures (DIMM-NMP) have received growing interest from both academia and industry. They have the advantages of large memory capacity, low manufacturing cost, high flexibility, compatible form factor, etc. However, inter-DIMM communication (IDC) has become a critical obstacle for generic DIMM-NMP architectures because it involves costly forwarding transactions through the host CPU. Recent research has demonstrated that, for many applications, the overhead induced by IDC may even offset the performance and energy benefits of near-memory processing.To tackle this problem, we propose DIMM-Link, which enables high-performance IDC in DIMM-NMP architectures and supports seamless integration with existing host memory systems. It adopts bidirectional external data links to connect DIMMs, via which point-to-point communication and inter-DIMM broadcast are efficiently supported in a packet-routing way. We present the full-stack design of DIMM-Link, including the hardware architecture, interconnect protocol, system organization, routing mechanisms, optimization strategies, etc. Comprehensive experiments on typical data-intensive tasks demonstrate that the DIMM-Link-equipped NMP system can achieve a 5.93× average speedup over the 16-core CPU baseline. Compared to other IDC methods, DIMM-Link outperforms MCN, AIM, and ABC-DIMM by 2.42×, 1.87×, and 1.77×, respectively. More importantly, DIMM-Link fully considers the implementation feasibility and system integration constraints, which are critical for designing NMP architectures based on modern DDR4/DDR5 DIMMs.
33.4 A 28nm 2Mb STT-MRAM Computing-in-Memory Macro with a Refined Bit-Cell and 22.4 - 41.5TOPS/W for AI Inference

Emerging non-volatile memory-based computing-in-memory (CIM) is an excellent fit for resource-constrained edge-AI devices [1–6]. MRAM-CIM macros for MAC operations, at present, rely on a crossbar structure or a peripheral circuit modification [2], [3]. It remains a great challenge for bottom-up design of MRAM-CIM macro using the standard one transistor - one magnetic tunnel junction (1T-1MTJ) bit-cell: (1) The mainstream spin-transfer-torque (STT) switching mechanism with a standard foundry bit-cell cannot fulfill CIM operation requirements in binary neural networks (BNN). (2) The excessive multi-row/column activation method suffers from a limited read window due to the limited tunnel magnetoresistance ratio and process variation [1], [2]. (3) Prior MRAM-CIMs rely on analog domain computing, for which an analog-to-digital converter is required with it's associated high energy consumption and area overhead [3], [6].
Differential Isolation Integrated Circuit Design in Galvanic-Coupled Human Body Communication

The galvanic-coupled human body communication (GCHBC) is a promising way of human communication with low power consumption and high stability. However, compared to other human communication methods, the GCHBC channel research has not determined an accepted and accurate measurement method. To provide a reference method for GCHBC channel research, this study proposes a differential isolation integrated GCHBC measurement scheme. The isolation module is introduced in the differential amplifier circuit to solve both the measurement accuracy problem and the common ground problem in the GCHBC channel characteristic measurement. The circuit has 43dB signal gain in the range of 100kHz-10MHz while achieving a common mode rejection ratio of over 73dB, satisfying the needs of human channel measurements. The measurement results of this circuit are compared with different receiver-side solutions, and the measurement accuracy is improved by about 5 dB. The measurement performance of this circuit is verified. It provides a receiver-side solution for the subsequent GCHBC channel characterization study.
19.1 A 300MHz-BW, 27-to-38dBm In-Band OIP3 sub-7GHz Receiver for 5G Local Area Base Station Applications

Recently, the so-called sub-6GHz band of the 5G new radio (NR) has been extended to 7.125GHz to address the relentless customer demand for higher data-rate communication. This demands a new design approach for the local area base-station (LA-BS) receivers (RXs) to cover a wide operating frequency range of 0.41 to 7.125GHz. Moreover, for NR bands above 3GHz, the maximum RF bandwidth (BW) is as high as 400MHz, in which a −35dBm modulated in-band (IB) blocker can be present. These impose stringent BW and IB linearity requirements for the baseband amplifiers in the LA-BS receivers. In addition to IB interferences, a −15dBm continuous-wave (CW) out-of-band (OOB) close-in blocker can also be present at 60MHz offset frequency from the passband edges, thus demanding a highly selective RX. Finally, the blocker 1dB compression point $(\mathrm{B}_{\mathrm{1}\text{dB}})$ becomes a key parameter for local area co-location applications in which the power of the far-out OOB blocker can be as large as −4dBm.
13.3 A Triturated Sensing System

Computer miniaturization has driven explosive evolution of our information society today, where compact high-performance computers exist everywhere to provide us easy and immediate access to powerful information technologies and services. To further advance our information society, one technology direction must be still pursuing the limit of this computer miniaturization. In this research, there always exist two technical challenges. The first is 1) limited function and performance scalability; Conventional miniature computers so far are basically designed only for a stand-alone operation, and thus struggling within a trade-off between physical size and function/performance limitations. For examples, 0.05mm-size miniature computer [1] integrates a small 128-bit NV memory block and functions as 1D generation only. A modular die-stack [2] can extend the scalability however its physical size increases to 1 mm according to additional function/performance. The second is 2) unstable energy source; Wireless power delivery or energy harvesting at GHz-band RF is one potential solution with an integrated on-chip antenna [3]. However, a very strict antenna matching condition or a strong EM absorber (e.g. a human body) finally restricts the deliverable energy amount and range. Ultrasonic backscattering is presented for the human body application [4] however a $\sim 1\text{mm}$ -size piezo device and strict position alignment with a bulky ultrasonic beamforming system is needed. In order to overcome above two challenges, not only circuit- but also system-level technical solutions are needed.
Predicting COVID-19 Case Counts using Twitter Image Data

A crucial task with diseases, such as COVID-19, is accurate forecasting of cases for early detection of spikes, which allows policymakers to adjust local restrictions. The use of face masks to prevent disease spread among the general population has become widespread due to the COVID-19 pandemic. While predictive models for COVID-19 case counts exist, capturing localized information about mask usage has the potential to improve prediction accuracy. In this paper, we develop time series models that utilize Twitter image data for COVID-19 case count prediction. A crucial part of such a model is the accurate detection of face mask presence in Twitter images, which we train a convolutional neural network (CNN) to perform. While multiple datasets exist to train CNNs for face mask detection, existing datasets do not adequately represent the complexity nor the diversity in social media images. To address this and create a sufficiently accurate CNN for use with social media images, we also present a new social media face mask image dataset designed for the training of CNNs to detect the presence of face masks in complex real-world images, such as social media images. The presented dataset consists of approximately 120k images and attempts to more adequately account for diversity in ethnicity, mask type, and physical orientation of individuals in images than existing datasets. We demonstrate the effectiveness of both the CNN model for face mask detection and the resulting time series model trained on data obtained from applying the CNN model to historical twitter data, illustrating that data on the presence of masks in social media images can increase predictive accuracy of time series models for COVID-19 case counts.
Predicting Indoor PM2.5 Concentration using LSTM-BNN in Edge Device

Many researchers already perform PM2.5 forecasting. However, the majority of research focuses on predicting PM2.5 concentrations in outdoor environments. In contrast, PM2.5 indoor prediction is rarely conducted, despite being more difficult. This study proposes an LSTM-BNN indoor PM2.5 concentration prediction model. The LSTM in the LSTM-BNN model extracts nonlinear correlations from multivariate time series input while the BNN predicts the PM2.5 concentration. Using multivariable input data, the proposed model estimates PM2.5 values 1 hour, 2 hours, and 3 hours in advance. In addition, the proposed model is compared to RNN, LSTM, Single Dense, Multi Dense, and ConvLSTM. MSE, RMSE, MAE, MAPE, and R2 are employed to evaluate the LSTM-BNN model objectively. The LSTM-BNN model beats other models with 1-hour, 2-hour, and 3-hour prediction MAPE and R2 values of 0.001 and 0.999, 0.004 and 0.996, and 0.004 and 0.999, respectively.
Magneto- Electric Dipole Antenna Array With Asymmetrical Beam Patterns at 26 GHz

A four-element millimeter wave (mmWave) array with asymmetrical beam patterns is designed. The array consists of two dual polarized ME dipole antenna and two single polarized ME dipole antenna. The transmitting array consists of four elements, receiving array consists of two elements and polarization direction is perpendicular to the transmitting. The S-parameters and transmitting beam scanning radiation pattern were measured and impedance bandwidth of array can cover 5G mmWave band (24.25-27.5 GHz). The maximum gain of transmitting array and receiving array are 13.2 dBi and 8.9 dBi.
OsiriX plugin for integrated cardiac imaging research

Strongly evolving imaging technologies such as magnetic resonance imaging (MRI) nowadays provide a multitude of new complementary techniques for the analysis of cardiovascular tissue properties, function, and hemodynamics. The purpose of the presented work is to provide a research tool, which enables a quick validation of newly developed imaging techniques and supports the co-development of clinically usable analysis tools, which allow an integration with existing complementary examination methods. The concepts combined to this end consist of an integration with the open source research PACS OsiriX, an advanced heuristic DICOM classification and preprocessing as well as an integrative data model, which accumulates patient-specific image data, results and the data relations. Specific processing and analysis plugins can easily be integrated in such a way that they use the data integration and visualization infrastructure as well as results from other existing plugins. The presented example applications, such as the evaluation of slice orientations for cardiac function quantification or the integrated analysis of different types of image data for diagnosis of myocarditis show that the provided tool can be successfully used for a multitude of research applications in cardiovascular imaging.
Performance Analysis of Optimal Perturbing Vector with Double Decomposition Precoding in Massive MIMO Systems

In a multi-user MIMO system, the application of the Vector Precoder is aimed at improving the system capacity by minimising the issues of power scaling. However, there is difficulty in acquiring the power scaling factor over a slow-fading environment, and this leads to increased transmission overhead. The complex signal selection from the lattice grid requires minimal optimisation and the highest precoding matrix vector selection requires minimal precoding matrix computation. In this research, an improved Vector Perturbation (VP) precoding is provided by introducing an MMSE solution to find the optimal perturbing complex signal vector. The complex signal in the lattice space is optimally obtained based on inaccurate channel state information (CSI). In the precoding matrix operation, we apply Hessenberg reduction to a QR Decomposition (HQRD) algorithm as it balances the trade-off between the vector precoding and computational complexity. The simulation is conducted to estimate the bit error rate (BER) using efficient fixed sphere detection based on varying channel conditions. The results show that the proposed vector precoder achieves a BER of 10−3 better than the state-of-the-art precoders.
A Message Passing Algorithm for Off-grid DOA Estimation Based on UWB System

The Ultra Wideband (UWB) technology has attracted extensive attention and is of great significance for positioning. Direction of arrival (DOA) estimation can improve positioning accuracy of UWB. At present, most researches on DOA estimation algorithms are based on sparse Bayesian learning (SBL) framework. This paper mainly proposes a solution for off-grid DOA estimate of UWB system. We introduce Dirichlet process (DP) prior to cluster the source UWB signals. To describe the problem, we design a factor graph. Mean field (MF) rule is used for the factor graph and a message passing algorithm is obtained. Experimental results prove that the algorithm proposed in this paper is superior.
A New Image denoising Technique Combining the Empirical Mode Decomposition with a Wavelet Transform Technique

This paper proposes a method for image denoising in the filter domain based on the characteristics of the Empirical Mode Decomposition (EMD) and the wavelet technique. The proposed method uses the EMD to the decomposition and double density wavelet to filter components. Our experimental results show that these image denoising methods are more efficient than the wavelet denoising method. Finally, the PSNR (peak signal noise ratio) and the visualization of the denoising image are used as performance comparison indexes.
VVQ: Virtualizing Virtual Channel for Cost-Efficient Protocol Deadlock Avoidance

Deadlock freedom is a critical component of interconnection networks in large-scale systems. In particular, protocol or high-level deadlock can occur from dependency based on network endpoints. Virtual channels (VCs) are commonly used to avoid such protocol deadlocks in large-scale systems; however, the cost of VCs is high in large-scale networks because of the deep input buffers and VCs need to be replicated. In this work, we propose to virtualize virtual channels to create Virtualizing Virtual Queues (VVQ) buffer architecture. VVQ is based on the observation that a FIFO buffer organization is sufficient when protocol deadlocks do not occur. However, when potential protocol deadlock can occur through blocking, VVQ takes a proactive approach to allow packets from different traffic classes to "jump" the queue and ensure protocol deadlock does not occur. Our proposal shows how a ghost pointer can be leveraged to enable such buffer organization without introducing complex buffer management such as dynamic buffer organizations. Our evaluations show VVQ can match the performance of the baseline VC buffer organization with only half of the total buffer storage.
Indoor Localization Based on Semi-tensor Product Compression Sensing

The sparsity of the localization problem makes the Compression Sensing (CS) theory suitable for indoor localization in Wireless Local Area Network (WLAN). However, when the target environment has a large number of Access Points (APs), online measurement takes a lot of time and increases the storage space of the AP selection matrix. To address this drawback, a Semi-tensor Product Compression Sensing (STP-CS) model is proposed, which reduces the time required to construct the AP selection matrix by constructing a low-order matrix, and enhances the real-time performance of online localization simultaneously. Experimental results show that, compared with the traditional methods, the proposed method has a great improvement in real-time performance and low overhead while ensuring localization accuracy.
Real-Time Facial Emotion Detection Through the Use of Machine Learning and On-Edge Computing

In this research study, we implemented a customized deep learning model for sentiment analysis through facial emotion detection to be used in real-time. This study aims to maximize the model’s accuracy and create a lightweight model for real-time Facial Expression Recognition (FER) on edge devices. In order to accomplish this goal, we researched the most recent models and techniques used for FER. We developed our fine-tuned model using FER2013, AffectNet, JAFFE, CK+, and KDEF datasets with 87.0% accuracy, comparable to the most accurate real-time models today, which range from 65-75% accuracy. The primary advantage of the proposed model is the simplified architecture which makes it lightweight and suitable to be deployed on various edge devices for real-time applications. We show how a lightweight but fine-tuned model can achieve higher accuracy than more complicated models proposed in recent studies.
A Compactly Arranged 4-Port Antenna Array with High Isolation

A compactly arranged 4-port antenna array with high isolation is presented in this letter. A novel decoupling loop is adopted in this antenna array, this proposed decoupling structure helps with improving the isolation between two adjacent antenna elements and capable of enhancing the bandwidth of the original antenna array. Operation band of this proposed antenna array includes 2.53-3.15 GHz and 5-6.05 GHz. In addition, size of this antenna array is merely 30mm*30mm*0.4mm, which is suitable for its application in cellular devices.
Adrias: Interference-Aware Memory Orchestration for Disaggregated Cloud Infrastructures

Workload co-location has become the de-facto approach for hosting applications in Cloud environments, leading, however, to interference and fragmentation in shared resources of the system. To this end, hardware disaggregation is introduced as a novel paradigm, that allows fine-grained tailoring of cloud resources to the characteristics of the deployed applications. Towards the realization of hardware disaggregated clouds, novel orchestration frameworks must provide additional knobs to manage the increased scheduling complexity.We present Adrias, a memory orchestration framework for disaggregated cloud systems. Adrias exploits information from low-level performance events and applies deep learning techniques to effectively predict the system state and performance of arriving workloads on memory disaggregated systems, thus, driving cognitive scheduling between local and remote memory allocation modes. We evaluate Adrias on a state-of-art disaggregated testbed and show that it achieves 0.99 and 0.942 R2 score for system state and application’s performance prediction on average respectively. Moreover, Adrias manages to effectively utilize disaggregated memory, by offloading almost 1/3 of deployed applications with less than 15% performance overhead compared to a conventional local memory scheduling, while clearly outperforms naive scheduling approaches (random and round-robin), by providing up to ×2 better performance.
Validation of surrogate end points in multiple randomized clinical trials with failure time end points

Before a surrogate end point can replace a final (true) end point in the evaluation of an experimental treatment, it must be formally ‘validated’. The validation will typically require large numbers of observations. It is therefore useful to consider situations in which data are available from several randomized experiments. For two normally distributed end points Buyse and co‐workers suggested a new definition of validity in terms of the quality of both trial level and individual level associations between the surrogate and true end points. This paper extends this approach to the important case of two failure time end points, using bivariate survival modelling. The method is illustrated by using two actual sets of data from cancer clinical trials.
20.1 A High Common-Mode Transient Immunity GaN-on-SOI Gate Driver for High dV/dt SiC Power Switch

Silicon carbide (SiC) MOSFET devices offer better performance in high-voltage (HV) and high-current applications, such as electric vehicles, railways, and motor drives due to their low losses, low impedance, high blocking voltage, and good high temperature tolerance. Recently, the rated 1700V SiC is used in a power conversion system (PCS) for efficient energy storage. Compared to 800V and 1200V SiCs, high voltage and fast switching SiC MOSFET will cause large dv/dt (> 100kV/µs) and di/dt $(>10\text{kA}/\mu\mathrm{s})$ High dv/dt at the switch node will couple the common mode transient (CMT) disturbances to the high side transmitter (TX) through an isolation barrier parasitic capacitance $\mathrm{C}_{\text{PAR}}$. Meanwhile, a large di/dt on the parasitic inductance $\mathrm{L}_{\text{PAR}}$ will cause abnormal ringing and couple to the TX through $\mathrm{C}_{\text{PAR}}$ (upper left of Fig. 20.1.1). Although the isolated gate drivers (IGD) with the transformer galvanic isolation have good common-mode transient immunity (CMTI) [1], their duty cycle is limited to 50% due the regeneration of the drive voltage being controlled by the duty cycle [2]. In contrast, capacitive isolation is not limited by the duty cycle, but instead, requires the modulator at the TX circuit to transmit the drive signal. When CMT disturbance occurs, a common mode current $(\mathrm{I}_{\text{CM}})$, proportional to dv/dt at the switching node, flows through $\mathrm{C}_{\text{PAR}}$ to the TX circuit and affects the conventional demodulation circuit of the receiver (RX) circuit (top in Fig. 20.1.1). The dv/dt ranges from over $100\text{kV}/\mu\mathrm{S}$ for 1700V SiC to 40kV/µs for 800V SiC. In the case of large $\mathrm{R}_{1(2)}$, the induced $\mathrm{I}_{\text{CM}}$ will cause large common-mode shift $(\text{CMS}=\mathrm{I}_{\text{CM}}\ ^{\star}\mathrm{R}_{1(2)})$. A large CMS can cause the $\mathrm{V}_{\text{IP}}$ or $\mathrm{V}_{\text{IN}}$ of the comparator in the demodulator to exceed the input common mode range (ICMR) and the demodulator cannot receive the correct control signal for some CMT periods. Due to the absence of correct control signals, fault tolerance mechanisms can limit the runtime within a hysteresis window [3]. However, under high voltage, the noise coupling to the gate of the low-side SiC MOSFET, through the gate-to-drain capacitance $\mathrm{C}_{\text{GD}}$, will become larger due to the higher dv/dt, which will increase the possibility of abnormal turn-on. State-of-the-art gate drivers require an external negative supply voltage, $\mathrm{V}_{\text{EE}}$ to turn off the SiC MOSFET to suppress abnormal turn-on. In addition, conventional discrete $\mathrm{V}_{\text{EE}}$ voltage sources are set more negative to overcome discrete parasitic resistance effects. However, efficiency is reduced if $\mathrm{V}_{\text{EE}}$ is too low.