Reviewerly/exHarmony huggingface dataset:
CUDA Quantum: The Platform for Integrated Quantum-Classical Computing

A critical challenge to making quantum computers work in practice is effectively combining them with classical computing resources. From the classical side of hybrid algorithms and integrated application workflows to decoding syndromes for quantum error correction, tightly coupled high performance classical computing will be important for many of the functions required to realize useful quantum computing. A key tool for enabling research and application development is a programming model and software toolchain which allow researchers to straightforwardly co-program classical and quantum computers and leverage the best tools available for each. NVIDIA CUDA Quantum is a single-source programming model in C++ and Python for heterogeneous quantum-classical computing. The CUDA Quantum platform provides several advantages and new capabilities that enable users to get more out of quantum processors. Here, we present CUDA Quantum and demonstrate several use cases including Variational Quantum Eigensolver (VQE) where it provides a significant (287x) performance and capability benefit over existing quantum programming.
Estimation of Solar PV Plant Output using LSTM-CNN Algorithm

Maintaining a Solar Photovoltaic plant incorporates difficulties such as periodic panel sanitizing, inverter maintenance, monitoring and testing, weather-related damage, battery maintenance (if necessary), and insect control. While solar facilities require less maintenance than traditional power plants, appropriate servicing is necessary for optimal performance and durability. To overcome these problems, we are presenting a precise forecast of solar plant electricity production since it is vital for effective grid management, optimal scheduling of maintenance of the plant, and cost-effective energy trading. A Long Short-Term Memory - Convolutional Neural Network based approach is presented in this study for solar plant production forecasting. This method has exhibited promising results in recent years for time-series forecasting. The proposed model combines Long Short-Term Memory and Convolutional Neural Network strengths to capture the input data’s long-term dependencies and spatial features. The proposed algorithm is tested on available dataset [2] and a root mean square error of 3.3% is obtained. This error is lower as compared to 5.5432% as mentioned in [16]; thus, our method produces better results.
Specialization meets Flexibility: a Heterogeneous Architecture for High-Efficiency, High-flexibility AR/VR Processing

Emerging AR-VR applications execute complex heterogeneous workloads, mixing Deep-Learning(DL) and Digital-Signal-Processing(DSP) tasks, on SoCs embedded in the frame of eyeglasses, with implied tight power and area constraints, especially in the case of AR. We propose ArchiMEDES, an open-source heterogeneous-SoC platform with a programmable cluster of RISC-V cores coupled with a configurable DNN engine (NEureka) targeting AR/VR workloads. ArchiMEDES features a low-overhead Heterogeneous Cluster Interconnect(HCI) to enable fast RISC-V/NEureka cooperation on a shared tightly coupled data memory (TCDM). We show post-layout results targeting 22nm technology; ArchiMEDES shows a peak combined performance of up to 1.19 TOPS and an efficiency of up to 10.6 TOPS/W. Hardware-Software cooperation in ArchiMEDES enables a 5.5× speedup in an AR-VR gaze tracking case study, compared to a non-cooperative single-RISC-V + Accelerator system.
Physics-aware Roughness Optimization for Diffractive Optical Neural Networks

As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural networks due to extreme fast computation speed (light speed) and low energy consumption. However, there is a mismatch, i.e., significant prediction accuracy loss, between the DONN numerical modelling and physical optical device deployment, because of the interpixel interaction within the diffractive layers. In this work, we propose a physics-aware diffractive optical neural network training framework to reduce the performance difference between numerical modeling and practical deployment. Specifically, we propose the roughness modeling regularization in the training process and integrate the physics-aware sparsification method to introduce sparsity to the phase masks to reduce sharp phase changes between adjacent pixels in diffractive layers. We further develop 2π periodic optimization to reduce the roughness of the phase masks to preserve the performance of DONN. Experiment results demonstrate that, compared to state-of-the-arts, our physics-aware optimization can provide 35.7%, 34.2%, 28.1%, and 27.3% reduction in roughness with only accuracy loss on MNIST, FMNIST, KMNIST, and EMNIST, respectively.
Hand Gesture Recognition Based on Deep Learning

This research paper focuses on the aspect of the interaction between human-computer and recognition of gestures. The paper explores three methods of contactless gesture recognition - vision-based, sound wave-based, and radio frequency signal-based - and highlights the vision-based method. However, the vision-based method is susceptible to interference from various factors, such as different palm sizes, skin colors, object occlusion, and self-occlusion. To address these issues, the paper proposes the use of deep neural networks for gesture recognition. The paper uses MediaPipe to detect 21 landmarks of hands and output their three-dimensional coordinates. The paper also explores four types of deep neural networks such as RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), hybrid (CNN and RNN), and Transformer Encoder - for identifying ten types of gestures (0 to 10). After optimization, the RNN model is selected, achieving an accuracy rate of 99.28%. Based on the detection of hand gestures, we can develop many practical applications of contactless interaction in the future.
CHAM: A Customized Homomorphic Encryption Accelerator for Fast Matrix-Vector Product

Homomorphic encryption (HE) is a promising technique for privacy-preserving computing because it allows computation on encrypted data without decryption. HE, however, suffers from poor performance due to enlarged data size and exploded amount of computation. Related work has been proposed to accelerate HE using GPUs, FPGAs, and ASICs. The existing work, however, aims at specific HE schemes and fails to consider the fast-evolving algorithms. For example, HE algorithms that combine different HE schemes have demonstrated capability of supporting more types of HE operations and ciphertexts. Moreover, some existing hardware accelerators target small HE operations (such as number theoretic transform and key-switch), which however provides limited or even neglected performance improvement for end-to-end applications. To better support existing privacy-preserving applications (e.g., logistic regression and neural network inference), we propose CHAM, an HE accelerator, for high-performance matrix-vector product, which can be easily extended to 2-D and 3-D convolutions. Motivated by the evolution of algorithms, CHAM supports not only traditional HE operations, but also different types of ciphertexts and the conversion between them. We implement CHAM with Xilinx FPGAs. The evaluation demonstrates 1800× speed-up for matrix-vector product, 36× speed-up for logistic regression, and 144× speed-up for Beaver triple generation compared to the existing work.
Lightning Talk 6: Bringing Together Foundation Models and Edge Devices

Deep learning models have been widely used in natural language processing and computer vision. These models require heavy computation, large memory, and massive amounts of training data. Deep learning models may be deployed on edge devices when transferring data to cloud is infeasible or undesirable. Running these models on edge devices require significant improvement in the efficiency by reducing the models’ resource demands. Existing methods to improve efficiency often require new architectures and retraining. The recent trend in machine learning is to create general-purpose models (called foundation models). These pre-trained models can be repurposed for different applications. This paper reviews the methods for improving efficiency of machine learning models, the rise of foundation models, challenges and possible solutions improving efficiency of pre-trained models. Future solutions for better efficiency should focus on improving existing trained models with no or limited training.
Power Side-Channel Vulnerability Assessment of Lightweight Cryptographic Scheme, XOODYAK

This work presents a power side-channel analysis (SCA) of a lightweight cryptography (LWC) algorithm, XOODYAK, implemented on an FPGA. First, we perform generic leakage detection tests for two phases of authenticated encryption with associated data (AEAD) mode, namely INITIALIZE, and ABSORB. Second, we develop novel hypothetical attack models for correlation power analysis (CPA) and demonstrate a success rate (SR) of 92%/82% and minimum-traces-to-disclosure (MTD)=13K/38K on the INITIALIZE/ABSORB phases, respectively. Third, we evaluate ABSORB against Profiled SCA using convolutional neural network (CNN), and achieve SR=96%/64% and MTD=2K/16K on the test set for the same/different keys used for training, respectively. Finally, we suggest low-overhead countermeasures to protect against these SCA attacks.
Map-and-Conquer: Energy-Efficient Mapping of Dynamic Neural Nets onto Heterogeneous MPSoCs

Heterogeneous MPSoCs comprise diverse processing units of varying compute capabilities. To date, the mapping strategies of neural networks (NNs) onto such systems are yet to exploit the full potential of processing parallelism, made possible through both the intrinsic NNs’ structure and underlying hardware composition. In this paper, we propose a novel framework to effectively map NNs onto heterogeneous MPSoCs in a manner that enables them to leverage the underlying processing concurrency. Specifically, our approach identifies an optimal partitioning scheme of the NN along its ‘width’ dimension, which facilitates deployment of concurrent NN blocks onto different hardware computing units. Additionally, our approach contributes a novel scheme to deploy partitioned NNs onto the MPSoC as dynamic multi-exit networks for additional performance gains. Our experiments on a standard MPSoC platform have yielded dynamic mapping configurations that are 2.1x more energy-efficient than the GPU-only mapping while incurring 1.7x less latency than DLA-only mapping.
Fake Website Detection Using Machine Learning Algorithms

Fake websites have become a growing concern in today’s digital age, as they are designed to deceive users into sharing personal and financial information. This research investigates the performance of Machine Learning algorithms, including Random Forest, LightGBM, and XGBoost, for detecting fake websites. We have utilized a categorical dataset with four types of websites: benign, defacement, phishing, and malware, and extracted several features from website content and metadata to train and test the algorithms. The results show that Random Forest achieved the highest accuracy (97%), outperforming both LightGBM (96%) and XGBoost (96.2%). This study highlights the effectiveness of using ensemble learning algorithms for detecting fake websites and continued research in this area can improve their performance and help safeguard against digital threats.
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning

Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks. However, the pre-trained models are often prohibitively large for delivering generalizable representations, which limits their deployment on edge devices with constrained resources. To close this gap, we propose a new transfer learning pipeline, which leverages our finding that robust tickets can transfer better, i.e., subnetworks drawn with properly induced adversarial robustness can win better transferability over vanilla lottery ticket subnetworks. Extensive experiments and ablation studies validate that our proposed transfer learning pipeline can achieve enhanced accuracy-sparsity trade-offs across both diverse downstream tasks and sparsity patterns, further enriching the lottery ticket hypothesis.
Quixote: Improving Fidelity of Quantum Program by Independent Execution of Controlled Gates

NISQ (noisy intermediate-scale quantum) computers are vulnerable to errors, which limit the size of verifiable quantum circuits. For large quantum circuits, it is more difficult to obtain reliable results due to errors. A circuit partitioning approach can improve fidelity by separating and reducing the size of circuits processed at once in NISQ devices. In this paper, we propose Quixote (quantum independent execution architecture), that can execute quantum circuits independently as subcircuits to improve the fidelity of NISQ program results. We present methods for decomposing controlled gates into independent subcircuits and additional techniques for reducing circuit costs through identical gate transformation.
Gap Image Classification Based on PCA-SVM with Multiple Feature Fusion

The detection of gap size at the threaded connection of the inner diameter of an aviation rocket body is crucial for the performance of the body. Due to the different processing degrees of the inner diameter during processing, there are two working conditions on the inner diameter surface. The image features of the gap under these two working conditions are different. Therefore, before detecting the gap size, it is necessary to distinguish between these two working conditions. This article proposes a multi feature fusion classification method based on PCA-SVM, which takes texture features, grayscale features, and depth features as joint feature parameters of the image. The PCA algorithm is used to perform multi feature fusion on them, and the fused feature parameters are input into the training SVM classifier for training the classification network. The experiment shows that the proposed method has a classification recognition rate of 99.5% in the classification of rough and fine surfaces of aviation rockets, Providing technical support for efficient and automated production of products.
Flex-SFU: Accelerating DNN Activation Functions by Non-Uniform Piecewise Approximation

Modern DNN workloads increasingly rely on activation functions consisting of computationally complex operations. This poses a challenge to current accelerators optimized for convolutions and matrix-matrix multiplications. This work presents Flex-SFU, a lightweight hardware accelerator for activation functions implementing non-uniform piecewise interpolation supporting multiple data formats. Non-Uniform segments and floating-point numbers are enabled by implementing a binary-tree comparison within the address decoding unit. An SGD-based optimization algorithm with heuristics is proposed to find the interpolation function reducing the mean squared error. Thanks to non-uniform interpolation and floating-point support, Flex-SFU achieves on average 22.3x better mean squared error compared to previous piecewise linear interpolation approaches. The evaluation with more than 700 computer vision and natural language processing models shows that Flex-SFU can, on average, improve the end-to-end performance of state-of-the-art AI hardware accelerators by 35.7%, achieving up to 3.3x speedup with negligible impact in the models’ accuracy when using 32 segments, and only introducing an area and power overhead of 5.9% and 0.8% relative to the baseline vector processing unit.
Joint Intensity and Event Framework for Vehicle Detection in Degraded Conditions

Images captured by traditional cameras inevitably suffer from quality degradation under adverse conditions (e.g., low-light, snowy and foggy), this will jeopardize the safety of the self-driving system. Unlike traditional cameras, event cameras can work stably in high dynamic range scenarios, capture accurate motion clues of objects in motion scenes. In this paper, we propose a RGB-Event fusion framework to achieve robust vehicle detection in degraded conditions. Firstly, we design a dual-stream feature extraction network to extract modality-specific features. Secondly, we propose a Feature Align Module (FAM) to align the feature space of the two modalities. Finally, we present a simulated RGB-Event dataset for vehicle detection. It contains variable weather and lighting conditions collected in Panosim. Experimental results on the proposed dataset show that our proposed method can outperform the SOTA framed-based method and RGB-Event method by 2.4%, 7.7% in mAP50 and 4.2%, 8.9% in mAP50:95, respectively.
Detection of Leukemia Using Inception-V3 and GoogLeNet

Leukemia is a type of cancer affecting the blood that usually involves the white blood cells, which are strong infection fighters. The purpose of this research is to find out the classification results of the two models, namely, GoogLeNet and Inception-V3 in classifying two classes of leukemia images (normal images and abnormal images). This study uses a trained model called a pretrained model from ImageNet. The results of this study indicate that both training and testing, these two models (obtain very good results, where each has 100% accuracy). In addition, in the assessment of accuracy, an assessment is also carried out using the confusion matrix where the results of both also get equally good results. that is 100%. In addition to the assessment based on accuracy, the assessment was also carried out based on the time needed for the model to classify leukemia cells. In terms of time measurement, GoogLeNet is faster than Inception-V3. This study can be concluded that these two models can classify very well but in in terms of time-based assessment, GoogLeNet is faster than Inception-V3.
An FPGA-Compatible TRNG with Ultra-High Throughput and Energy Efficiency

In this paper, we design an energy-efficient true random number generator with ultra-high throughput for FPGA. Only four ring oscillators constructed using eight LUTs are sampled by multiple sampling points to fully exploit the randomness of the entropy source, which provides high-quality and over 275 Mbps random sequences while consuming 13 slices. An end-to-end implementation and testing framework is tailored for easy deployment and portability on Xilinx 7 serials FPGAs. The proposed architecture passes the NIST SP 800-22 and 800-90B tests without post-processing and outperforms the state-of-the-art in terms of minimum entropy and energy efficiency.
Research on Endoscopic Image Depth Estimation Method Based on Deep Learning

Depth estimation of endoscopic images has always been a hot and difficult problem in minimally invasive surgery. Accurately estimating the depth information of endoscopic images can help surgeons improve the efficiency of surgical operations. This paper proposes a depth estimation method for endoscopic images based on deep learning, the ResNet34 module, FPN module and position information enhancement module are introduced to extract features. The reprojection loss, structural similarity index and edge-aware smoothing loss are used as loss function to improve the accuracy of depth prediction model and improve the applicability of the proposed method. The evaluation experiment uses the endoscopic image dataset, and the experimental results show that the depth information obtained by our method is more accurate than other state-of-the-art methods, which further verifies the effectiveness and advantages of the proposed method.
3D Object Detection with Fusion of Max-Sum Encoding and Gaussian Filtering

Environmental perception plays a key role in ensuring the operational safety of self-driving vehicles. It involves sensing spatial information such as the size, location, orientation of car, pedestrian and cyclist in the surrounding environment. Detection algorithms that fuse LiDAR point clouds and images can effectively improve the sparsity defect of point cloud data and the sensitivity of image data to harsh environment, but most fusion detection algorithms have low detection accuracy for small objects such as pedestrian and cyclist. To enhance the perception of small objects, we propose a novel 3D object detection model that integrates Max-Sum encoding and Gaussian filtering into the existing F-PointNet method. By incorporating the Max-Sum encoding module, we effectively aggregate the local fine-grained features and the maximum response information of the point cloud, solving the problem that the F-PointNet network only retains the maximum response but lacks local fine-grained features; we propose a Gaussian distance filtering based instance segmentation strategy to calculate the Gaussian distance between the point cloud and the center of the 2D object, and incorporate the Gaussian distance feature into the instance segmentation, improving the problem of low accuracy of instance segmentation in F-PointNet networks. To validate our algorithm, we conducted experiments in the KITTI and DAIR-V2X datasets. The results show that our algorithm outperforms the F-PointNet benchmark method in general in terms of car, pedestrian and cyclist detection results. Notably, it shows the most significant improvement for small objects such as pedestrian and cyclist, with improvements of 1.47% and 3.87% at the KITTI medium difficulty level, 4.44% and 2.06% at the DAIR-V2X medium difficulty level. In summary, our proposed algorithm effectively solves the low accuracy problem of instance segmentation and small objects detection in F-PointNet networks, and has strong applicability.
Trajectory Generation for Autonomous Surface Vessels Based on Bézier Curves

Autonomous surface vessels have attracted significant attention from scholars in the shipping and marine community in recent years. Researchers have focused on methods that consider the dynamics of the vessel to develop effective trajectory generation strategies for these vessels. Efficient trajectory generation is indispensable, but conventional path planning algorithms based on search and sampling are less applicable. To plan trajectories exclusively in the flat output space, we utilize the differential flatness property. In this paper, we use the basic artificial potential field method for front-end trajectory search and present a method for generating feasible trajectories for these vessels using piecewise Bézier curves. By incorporating Bernstein polynomial basis functions and optimizing the cost function with necessary constraints, we convert the problem into a typical convex quadratic programming format. Finally, we evaluate the efficacy of the method by conducting numerical simulation experiments.
Air conditioning fan optimization based on improved SO algorithm

This paper studied the air handling system with high internal energy consumption in air conditioning system. Most variable-frequency fans in the system were controlled by PID controller. At present, algorithms applied to PID controller emerge in endlessly. Most algorithms were either too complex, or had too many iterations and taken too long to calculate. This paper attempted to use a new simple and effective SO(Snake optimizer) algorithm to control, and analyzed its control and energy-saSving effect.