Miracle-dz/UniFAR-FTUs huggingface dataset:
PIC-RAM: Process-Invariant Capacitive Multiplier Based Analog In Memory Computing in 6T SRAM

In-Memory Computing (IMC) is a promising approach to enabling energy-efficient Deep Neural Network-based applications on edge devices. However, analog domain dot product and multiplication suffers accuracy loss due to process variations. Furthermore, wordline degradation limits its minimum pulsewidth, creating additional non-linearity and limiting IMC's dynamic range and precision. This work presents a complete end-to-end process invariant capacitive multiplier based IMC in 6T-SRAM (PIC-RAM). The proposed architecture employs the novel idea of two-step multiplication in column-major IMC to support 4-bit multiplication. The PIC-RAM uses an operational amplifier-based capacitive multiplier to reduce bitline discharge allowing good enough WL pulse width. Further, it employs process tracking voltage reference and fuse capacitor to tackle dynamic and post-fabrication process variations, respectively. Our design is compute-disturb free and provides a high dynamic range. To the best of our knowledge, PIC-RAM is the first analog SRAM IMC approach to tackle process variation with a focus on its practical implementation. PIC-RAM has a high energy efficiency of about 25.6 TOPS/W for $4-\text{bit}\times 4-\text{bit}$ multiplication and has only 0.5% area overheads due to the use of the capacitance multiplier. We obtain 409 bit-wise TOPS/W, which is about 2× better than state-of-the-art. PIC-RAM shows the TOP-1 accuracy for ResNet-18 on CIFAR10 and MNIST is 89.54% and 98.80% for $4bit\times 4bit$ multiplication.
Reliable Brain-inspired AI Accelerators using Classical and Emerging Memories

By taking inspiration from the operation of biological brains, emerging brain-inspired hardware has the potential to revolutionize the way computations are performed. Brain-inspired computing can be realized using both classical CMOS and emerging beyond-CMOS technologies, whereas the latter holds the promise to provide substantial energy savings akin to the employment of non-volatile memories. One way to implement highly efficient brain-inspired AI applications is through analog computing schemes, such as Integrate-and-Fire (IF) Spiking Neural Networks (SNNs), which can be implemented using both CMOS and beyond-CMOS technologies as synaptic storage. However, managing the inherent degradation of computing accuracy in analog circuits and mitigating their effects on the predictive accuracy of AI systems remains a key challenge due to the inherent nature of analog computing.In this paper, we discuss how the aforementioned challenges can be addressed. In the first part, we present our SPICE-Torch, a framework that connects low-level SPICE simulations of circuits and memories performing analog computations with high-level accuracy evaluations of NN models based on PyTorch. Furthermore, we present an example of neuromorphic optimization using classical CMOS technology. In the second part, we introduce memristors as an emerging beyond-CMOS technology that can retain their state without any outside influence and are well-suited for brain-inspired neuromorphic hardware. We demonstrate that brain-inspired hardware, realized using classical CMOS or beyond-CMOS technologies, has the potential to revolutionize the way we process information and solve complex computation problems. Nevertheless, to harness its full potential, reliability issues have to be managed carefully and HW/SW codesign is key. Our presented framework SPICE-Torch, which connects low-level SPICE simulations of circuits performing analog computations with high-level accuracy evaluations of NN models based on PyTorch is available as open-source in https://github.com/myay/SPICE-Torch.
Traffic Flow Prediction Based on Transformer and Multi-Spatial-Temporal Encoder-Decoder

Traffic flow prediction is one of the key research directions of intelligent transportation systems, and accurate traffic flow prediction can effectively reduce traffic congestion and improve road utilization. With the rapid development of artificial intelligence, many machine learning and deep learning models have been successfully applied to traffic flow prediction. Due to the many factors influencing traffic flow, the extremely complex nonlinear relationships between these factors, and the complex temporal and spatial correlations of traffic flow, traffic flow prediction is considered a challenging problem. In this paper, a new Multi-Spatial-Temporal Encoder-Decoder Model (MST-EDM) based on Transformer is proposed to capture the temporal correlation of traffic flow more accurately. Weekly time series encoder block, daily time series encoder block and adjacent time series encoder block are used to extract weekly time series correlation, daily time series correlation and adjacent time series correlation. The temporal correlation of different time slices is fused by feature. Experiments were conducted using the PEMS04 highway datasets from the Caltrans Performance Measurement System. The results show that our model significantly outperforms state-of-the-art baselines.
Attacking ReRAM-based Architectures using Repeated Writes

Resistive random-access memory (ReRAM) is a promising technology for both memory and for in-memory computing. However, these devices have security vulnerabilities that are yet to be adequately investigated. In this work, we identify one such vulnerability that arises from the write mechanism in ReRAMs. Whenever a cell/row is written, a constant bias is automatically applied to the remaining cells/rows to reduce sneak current. We develop a new attack (referred as WriteHammer) that exploits this process. By repeatedly exposing a subset of cells to this bias, WriteHammer can cause noticeable resistance drift in the victim ReRAM cells. Experimental results indicate that WriteHammer can cause up to 3.5X change in cell resistance by repeatedly writing to the ReRAM cells for a duration of 4 ms.
P-PIM: A Parallel Processing-in-DRAM Framework Enabling Row Hammer Protection

In this work, we propose a Parallel Processing-In-DRAM architecture named P-PIM leveraging the high density of DRAM to enable fast and flexible computation. P-PIM enables bulk bit-wise in-DRAM logic between operands in the same bit-line by elevating the analog operation of the memory sub-array based on a novel dual-row activation mechanism. With this, P-PIM can opportunistically perform a complete and inexpensive in-DRAM RowHammer (RH) self-tracking and mitigation technique to protect the memory unit against such a challenging security vulnerability. Our results show that P-PIM achieves ~72% higher energy efficiency than the fastest charge-sharing-based designs. As for the RH protection, with a worst-case slowdown of ~0.8%, P-PIM archives up to 71% energy-saving over the SRAM/CAM-based frameworks and about 90% saving over DRAM-based frameworks.
Exploring Planetary System Evolution Through High-Contrast Imaging

High-contrast imaging of circumstellar debris disks and exoplanets provides unique information about planetary system evolution. Several hundred nearby main-sequence stars are known to host debris disks: reservoirs of rock and ice that are thought to be coeval with planet formation. My dissertation research focused on detecting and characterizing such disks to gain new insight into circumstellar environments during the epoch of planet assembly. To view these systems on spatial scales down to a few astronomical units, I used near-infrared angular differential imaging data from the Keck NIRC2 and Gemini Planet Imager instruments. Detection of relatively faint disks and planets in these data is enhanced by image-processing algorithms that suppress the stellar signal, however, they also bias the brightnesses of these objects. I developed a new technique to forward-model and correct for this bias, thus allowing more accurate analysis of disk and planet properties. Applying this technique to scattered-light imaging of the HD 32297 debris disk, I used photometric and morphological measurements to infer physical characteristics of the disk and its constituent grains. I also investigated the HD 61005 debris disk and the role that an eccentric, inclined planet could play in shaping its unusual morphology. To expand the small samples of resolved disks and directly-imaged planets, I conducted a coronagraphic survey of 24 nearby stars, the preliminary results of which are reported herein. Concluding on the same theme of high-angular resolution imaging, my final project comprised tasks translating science requirements into design specifications for an upgraded Keck OSIRIS imager.
Construire un noyau de la fonctorialité ? Le cas de l’induction automorphe sans ramification de GL$_1$ à GL$_2$

Le but de cet article est de presenter une nouvelle methode purement adelique pour realiser le principe de fonctorialite de Langlands dans le cas de l'induction automorphe sans ramification de GL 1 a GL 2 sur les corps de fonctions. On construit sur le produit des groupes adeliques GL 1 et GL 2 un noyau de la fonctorialite. C'est une version "en famille" et locale de la construction par les modeles de Whittaker globaux, utilisee classiquement dans les "theoremes reciproques" de Weil et Piatetski-Shapiro. La plus grande partie de la construction et des verifications necessaires est locale, c'est-a-dire se fait place par place. Il s'agit de prouver que deux fonctions, dont chacune est definie localement, deviennent egales apres sommation sur les elements rationnels de certains groupes. Cela resulte de la formule de Poisson, sur le modele de la these de Tate, des lors que l'on comprend comment nos deux fonctions se deduisent localement l'une de l'autre par une certaine transformation de Fourier.
Multimodal biometric authentication using Fully Homomorphic Encryption

In this paper multimodal biometric system is developed using two traits iris and fingerprint. The features generated by iris and fingerprint images are fused at the feature level. The generated fused feature vector template cannot be stored directly on the server, if stored directly can lead to various privacy and security concerns. So, these templates are encrypted in such a way that even when applying any operations on the templates, the templates should be in encrypted form. So, the operations need to be performed in the encrypted domain without decrypting it, and the final result, when decrypted should again give back the correct result as if the operations are performed on the original data. Fully Homomorphic encryption (FHE) scheme is designed to satisfy the above conditions. FHE is used to compute the hamming distance between the reference and probe template in an encrypted domain. To improve accuracy rotational invariant technique is used, which solves rotational inconsistency problems. The computational speed is increased by using a batching scheme to reduce the number of operations during homomorphic multiplication. We have conducted our experiment on the IITD and CASIA dataset. The best EER is obtained in CASIA dataset of 0.01% with a computational time of 0.0152 sec per template.
A New Debleeding Algorithm for Image colorization

Colorization is the process of adding color to grayscale images using computer algorithms. There are several approaches to color an image, including precise image segmentation algorithms, deep learning algorithms, and manually colored local color expansion methods. However, one common problem with these approaches is the occurrence of “color bleeding”, where colors from one region of the image spill over into adjacent regions. In this paper, we propose a new manually colored local color expansion algorithm that considers the intensity value difference and distance difference between the central pixel in the window and its neighbor pixels comprehensively. Combined with side window filtering, our algorithm significantly reduces the occurrence of colors bleeding at the edges of the colored image. The experiments demonstrate the effectiveness of the proposed algorithm.
A Fast Method to Find Nodes with the Hightest Closeness Centrality Ranking in Directed BA Networks

It is widely known that a few hubs will emerge during the evolution process of the BA network, due to its Growth and Preferential Attachment mechanism, making this kind of network to be small-world and scale-free. From the feature of directed BA networks, we find the relation between the reachability of nodes and closeness centrality of nodes, thus provide a fast method to find nodes with the highest closeness centrality. The results of this method are verified using some real BA networks.
Enhancing the Sum Rate in Massive MIMO Optical Wireless Communication System by Using MMSE Precoding

We investigate an optical wireless communications system based on massive multiple-input multiple-output (MIMO). Through a transmit lens, the optical base station with its huge optical transmitters communicates with the user terminals (UTs). We create a channel model for optical massive MIMO transmissions and examine light refraction caused by lenses, with an emphasis on LED transmitters. We formulate the sum rate maximization problem and convert it into a convex optimization problem to get a closed-form solution. MMSE is used to increase the sum rate and Karush-Kuhn-Tucker (KKT) conditions were used to solve the aforementioned problem. We study traditional precoding techniques, maximum ratio transmission (MRT), and regularized zero-forcing precoding (RZF) for an optical massive MIMO system. The proposed technique is ajoint precoder design to enhance the sum rate. The simulation results show that the proposed MMSE precoder design outperforms MRT and RZF precoders in a massive MIMO optical wireless communication system.
Cross Layer Design for the Predictive Assessment of Technology-Enabled Architectures

There is great interest in “end-to-end” analysis that captures how innovation at the materials, device, and/or archi-tectural levels will impact figures of merit at the application-level. However, there are numerous combinations of devices and architectures to study, and we must establish systematic ways to accurately explore and cull a vast design space. We aim to capture how innovations at the materials/device-level may ultimately impact figures of merit associated with both existing and emerging technologies that may be employed for either logic and/or memory. We will highlight how collaborations with researchers at these levels of the design hierarchy - as well as efforts to help construct well-calibrated device models - can in-turn support architectural design space explorations that will help to identify the most promising ways to use new technologies to support application-level workloads of interest. For given compute workloads, we can then quantitatively assess the potential benefits of technology-driven architectures to identify the most promising paths forward. Because of the large number of potentially interesting device-architecture combinations, it is of the utmost importance to develop well-calibrated analytical modeling tools to more rapidly assess the potential value of a given (likely heterogeneous) solution. We highlight recent efforts and needs in this space.
Accident Injury Severity Prediction Research Based on Stacking Model

To mitigate the accident injury severity of intelligent vehicles when faced with the inevitable collision accident scenario caused by the intrusion of other traffic participants, it is critical to build an applicable accident injury severity prediction model. A two-layer Stacking framework is proposed in this study to predict the accident injury severity: The first layer integrates the advantages of four base classifiers: KNN (K Nearest Neighbors), RF (Random Forests), AdaBoost (Adaptive Boosting), and XGBoost (Extreme Gradient Boosting); The second layer Logistic Regression model is trained based on the outputs of the first layer and completes the final classification of accident injury severity. A total of 23995 samples released by the National Automotive Sampling System Crashworthiness Data System (NASS-CDS) were used in this study. The inputs of the model are accident-related features, which are obtained by vehicle sensors (Lidar, Radar, and Camera). The output is the highest injury level of the passengers in the vehicle, which includes 0 (non-injury), 1 (Minor injury), 2 (Serious injury), and 3 (Death). Experimental results show that the classification precision of this method reaches 84.08%, and it is superior to other commonly used models in precision, recall, and F1Score, which can be applied to the research of road traffic safety.
Simulation and Analysis of Multiple Traffic Time-Triggered Ethernet based on OMNeT++

Avionics full-duplex switched Ethernet (AFDX), as the backbone network in civil avionics systems, can transmit event-based device messages. However, with the increase in real-time business requirements of avionics systems, numerous key data need to use clock synchronization at the sender to comprehensively schedule flows of different trigger mechanisms, and expand the high-bandwidth communication architecture to meet Quality of Service (QoS). This paper use OMNeT++ simulator to establish the End System (ES) and switch network topology which adopts clock synchronization on the basis of Time-Triggered Ethernet (TTE). We first simulate the End-to-End Delay of Rate Constrained traffic for each Virtual Link (VL) and compare simultaneously other two approaches to limit the End-to-End latency of AFDX, the results show that the Network Calculus (NC) and Trajectory Approaches (TA) increase in pessimism in most cases. Then we expand the Gigabit network topology of each message scheduling communication paradigm, compared with the traditional AFDX scheduling strategy, and verify that table scheduling can solve Sequence Number (SN) inversion to a certain extent. The experimental results also show that the table scheduling strategy based on time reservation can not only provide time triggered traffic planning establishes transfers to ensure low latency and minimal jitter, while also reducing worst-case end-to-end latency for Rate-Constrained (RC) and Best-Effort (BE) flows.
Aircraft Trajectory Prediction Model Based on Improved GRU Structure

In view of the actual need to predict aircraft trajectory, traditional prediction models often have problems such as insufficient precision and slow training efficiency. By analyzing the target trajectory with temporal characteristics, the Elastic-BiGRU trajectory prediction model is proposed, which combines the Smooth filtering method, the Elastic Network fitting method and the GRU structure, the prediction accuracy of aircraft trajectory is further improved. The experimental results show that the Elastic-BiGRU model compared with Bi-LSTM model and Bi-GRU model, its MSE error is relatively reduced by more than 8% and 11%The Elastic-BiGRU also solves the problem of slow training speed of Bi-LSTM model, and saves about 20% of the time.
An Byzantine fault-tolerant consensus algorithm based on MuSig

In view of the disadvantages of PBFT consensus algorithm such as large traffic and complicated view change, a Byzantine fault-tolerant consensus algorithm based on MuSig, namely MSBFT consensus algorithm, is proposed. The algorithm adopts multi-signature MuSig and pipeline idea, and optimizes the consensus process, view change protocol and checkpoint protocol. The communication complexity of the consensus process, view change protocol, and check point protocol in MSBFT is reduced from O(n2), O(n3), and O(n2) in PBFT to O(n) respectively. Experimental results show that compared with ABFT, PBFT and SBFT, the consensus efficiency and throughput of MSBFT algorithm are greatly improved.
Hardware Efficient Weight-Binarized Spiking Neural Networks

The advancement in spiking neural networks (SNNs) provides a promising and alternative approach to conventional artificial neural networks (ANNs) with higher energy efficiency. However, the significant requirements on memory usage presents a performance bottleneck on resource constrained devices. Inspired by the notion of binarized neural networks (BNNs), we incorporate the design principles in BNNs into that of SNNs to reduce the stringent resource requirements. Specifically, the weights are binarized to 1 and -1 for implementing the functions of excitatory and inhibitory synapses. Hence, the proposed design is referred to as a weight-binarized spiking neural network (WB-SNN). In the WB-SNN, only one bit is used for the weight or a spike; for the latter, 1 and 0 indicate a spike and no spike, respectively. A priority encoder is used to identify the index of an active neuron as a basic unit to construct the WB-SNN. We further design a fully connected neural network that consists of an input layer, an output layer, and fully connected layers of different sizes. A counter is utilized in each neuron to complete the accumulation of weights. The WB-SNN design is validated by using a multi-layer perceptron on the MNIST dataset. Hardware implementations on FPGAs show that the WB-SNN attains a significant saving of memory with only a limited accuracy loss compared with its SNN and BNN counterparts.
Applying Hybrid Decision-Making Method Based on Fuzzy AHP-WOWA Operator for Emergency Alternative Evaluation of Unattended Train Operation Metro System

Optimal alternative selection to address the emergency situation is critical for dispatcher group in Unattended Train Operation (UTO) to guide emergency process. It is difficult to provide the precise decision value under one criterion and to evaluate the emergency alternatives among multiple dispatchers. This paper presents a hybrid emergency decision-making method integrating fuzzy analytic hierarchy process (FAHP) described by linguistic terms with enhanced weighted ordered weighted averaging (WOWA) operator. The enhanced WOWA operator aggregates the preference matrices of multidispatcher through the constructed emergency response task model of dispatcher group in OCC. This calculation approach takes into consideration the relations of emergency tasks to derive the importance weights of dispatchers and integrates them into the ordered weighted averaging (OWA) operator weights based on a fuzzy membership relation. A case study of applying the method in an emergency of a train fire is given to demonstrate the feasibility and usefulness of the methods associated with the group multicriteria decision-making (GMCDM) theory in emergency management of UTO metro system.
Modified Weighted Compact Scheme for Shock-Boundary Layer Interaction and Double Cone

The critical problem of CFD is perhaps an accurate approximation of derivatives for a given discrete data set. Based on our previous work on the weighted compact scheme (WCS), a modified weighted compact scheme (MWCS) has been developed. Similar to WENO, three high order candidates, left, right, and central, are constructed by Hermite polynomials. According to the smoothness, three weights are derived and assigned to each candidate. The weights will lead the scheme to be bias when approaching the shock or other discontinuities but quickly becomes central, compact, and of high order just off the shock. Therefore, the new scheme can get a sharp shock without oscillation, but keep central, compact and of high resolution in the smooth area. This feature is particularly important to numerical simulation of the shock-boundary layer interaction, where both shock and small eddies are important. The new scheme has three compact candidate stencils, 2 1 0 , , E E