Reviewerly/exHarmony huggingface dataset:
DmTSAR-ILP: Allocating a Unified Domain Platform for Streaming Applications

Domain-specific HWACC-rich platforms blend high performance & efficiency presenting an opportunity to recover non-recurring engineering costs through wider deployment for many applications. However, the design of such platforms is immensely challenging, partly due to the design space size.MG-DmDSE [1] allocates domain platforms catering to multiple applications. To cope with complexity, it employs a genetic algorithm and heuristics. While this is a typical approach in DSE, it cannot guarantee optimality. Exact solutions could be obtained with integer linear programming (ILP). Yet, the multi-application DSE presents aggregation challenges as applications might have widely different performance, leading to non-linear aggregation techniques in previous work that ILPs cannot employ.This work introduces DmTSAR-ILP, an ILP-based platform allocation method that simultaneously considers all applications in a domain. DmTSAR-ILP captures the same underlying analytical model as MG-DmDSE and can explore its domain application set. To enable our linear domain-level formulation, we introduce the average performance achievement metric (APA), offering an ILP-friendly, fair aggregation across domain applications.To highlight benefits of DmTSAR-ILP, a domain platform for 40 OpenVX apps is generated (across varying area budgets). For all area budgets, DmTSAR-ILP platforms perform better or identical to MG-DmDSE even in their original metric. The proposed APA aggregation is fair, allowing more applications (55% vs 37.5% of applications in MG-DmDSE) to be fully accelerated on the generated platform. For the 0.1 mm 2 area budget, DmTSAR-ILP’s platform increases application throughput by 22.5% over MG-DmDSE’s platform. DmTSAR-ILP is 70x faster on average than the heuristic-based MG-DmDSE by leveraging an ILP formulation that considers all applications simultaneously and recent advances in MIP solver performance.
Late Breaking Results: Test Selection For RTL Coverage By Unsupervised Learning From Fast Functional Simulation

Functional coverage closure is an important but RTL simulation intensive aspect of constrained random verification. To reduce these computational demands, we propose test selection for functional coverage via machine learning (ML) based anomaly detection in the structural coverage space of fast functional simulators. We achieve promising results on two units from a state-of-the-art production GPU design. With our approach, an up to 85% RTL simulation runtime reduction can be achieved when compared to baseline constrained random test selection while achieving the same RTL functional coverage.
A Scoping Review of GAN-Generated Images Detection

The usage of Generative Adversarial Network (GAN) architectures has given anyone an ability to generate an image that is indistinguishable from the real image. The improper use of GAN-generated images may lead to serious privacy, security, political, and social consequences such as spreading of fake information and legal issue. Therefore, it is crucial to emphasize the widespread of fake imaginary by developing a fake image detection system. Convolutional Neural Network (CNN) is traditional method in detecting GAN-generated images. However, due to the advancement and variations of GAN, CNN often suffer from limited generalization. Benford’s law can also be applied to produce features that can be used to detect GAN-generated images. In this paper, the fundamentals of GAN, and the technology used in fake image detection model will be discussed and reviewed thoroughly.
ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.
Measurement and forecasting of fluctuating Cryptocurrency prices using deep learning

In the context of cryptocurrency forecasting, this paper provides a comprehensive analysis of various prediction methods, including financial methods, statistical methods, machine learning, and deep learning. It investigates the causes of the effectiveness of the most well-known and accurate techniques. In addition, the study compares the results of its RMSE, MAE, and MAPE to those of other studies that have used the same dataset with same time period. This study investigates the effect of different evaluation matrices on the accuracy of models and compares the performance of two distinct cryptocurrencies on various deep-learning models. By analyzing the relationship between evaluation metrics and the accuracy of price predictions, the study aims to facilitate the development of more precise models for predicting the prices of cryptocurrencies. This study adds to the literature on cryptocurrency forecasting by evaluating several approaches to see which methods provide the most reliable results. Researchers and practitioners can make informed decisions regarding the development and application of cryptocurrencies if they comprehend the factors that contribute to the accuracy of cryptocurrency prediction models. In addition, the study emphasizes how bi-LSTM and LSTM can be used to forecast various cryptocurrencies and how price fluctuations can be measured and predicted with an accuracy level greater than 80%. Overall, this study contributes to the advancement of knowledge and the development of cryptocurrency price prediction methods, thereby augmenting decision-making processes in cryptocurrency markets.
When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA

Bayesian Neural Networks (BayesNNs) have demonstrated their capability of providing calibrated prediction for safety-critical applications such as medical imaging and autonomous driving. However, the high algorithmic complexity and the poor hardware performance of BayesNNs hinder their deployment in real-life applications. To bridge this gap, this paper proposes a novel multi-exit Monte-Carlo Dropout (MCD)-based BayesNN that achieves well-calibrated predictions with low algorithmic complexity. To further reduce the barrier to adopting BayesNNs, we propose a transformation framework that can generate FPGA-based accelerators for multi-exit MCD-based BayesNNs. Several novel optimization techniques are introduced to improve hardware performance. Our experiments demonstrate that our auto-generated accelerator achieves higher energy efficiency than CPU, GPU, and other state-of-the-art hardware implementations. Our code is publicly available at: https://github.com/os-hxfan/MCME_FPGA_Acc.git
Development of U-Net Architecture for Audio Super Resolution

Audio processing is used in a wide range of applications, including telecommunications and music streaming. Audio quality degradation during transmission and processing is a common issue in these fields, often caused by bandwidth constraints and the use of subpar equipment. This problem is exacerbated when the task requires converting low-quality audio input to high-resolution output, which is difficult for deep neural networks to do. To improve audio superresolution, this paper proposes a novel solution to this problem by embedding a U-Net-based architecture model within deep neural networks. Over 100 iterations, the U-Net architecture was trained, with loss values and Mean Squared Error (MSE) monitored at each epoch. A diverse dataset of audio signals with varying Signal-to-Noise Ratio (SNR) values ranging from 1 dB to 30 dB was used. The model’s average SNR of 17.29 dB exceeds thresholds where listener detection of enhancements becomes difficult, demonstrating its ability to preserve subtle audio details. Furthermore, the Log-Spectral Distance (LSD) values revealed a mere 1.41 dB difference between the actual and reconstructed spectrograms, indicating that the model can recover lost information during upsampling. This research suggests a promising method for improving audio quality, particularly when bandwidth constraints or insufficient equipment prevent high-resolution audio transmission and processing.
Invited: Pre-silicon Side Channel and Fault Analysis

In this work, we address the challenges of side channel analysis (SCA) and fault injection (FI) in the pre-silicon design stage, particularly for cryptographic implementations. We show that the industry perspective uncovers different challenges than those usually tackled by academia, emphasizing the need for practical and scalable solutions. We present (Side Channel Attack Testbench Emulator) SCATE, a framework for detecting and mitigating SCA vulnerabilities, leveraging industry-standard electronic design automation (EDA) software for efficient power estimation and FI simulations. Moreover, we demonstrate a significant performance increase in power simulation tools by optimizing EDA algorithms for SCA applications, resulting in faster power trace generation. Additionally, we discuss the application of formal property verification (FPV) for verifying fault injection countermeasures and present a case study on an AES S-box. These approaches enable non-security-expert designers to evaluate SCA and FI resistant designs with significant time savings. The vulnerabilities uncovered by these approaches highlight the importance of pre-silicon analysis at each design stage to produce secure application-specific integrated circuits (ASICs).
E-Voting System Improvised by Blockchain Technology: A Case Study

For a very long time, it has been a difficult task to construct a voting machine system that is both secure and fair, maintaining the confidentiality of the various voting methods that are currently in use while also maintaining the accountability and adaptability of the electrical components. This piece of work presented a procedure for an election in a faraway place that must be secure, deniable, irrevocable, user-friendly, and convenient. As a consequence of this, members of the voting public can vote for their preferred candidate without being required to carry out the procedure in person. Campaigns for the upcoming election will make use of this framework. This system will make use of two different blockchains, both of which will communicate with one another: the Ethereum platform, which is accessible to the general public, and the Quorum blockchain, which is not. The technology of private blockchains will be operated by an autonomous organization. All of these different blockchain technologies send the necessary data to the public blockchain technology, which seems to be able to process a variety of voting data. This framework makes it possible to use encrypted information effectively with the SHA-256 method, thereby protecting both the confidentiality of the information and the knowledge it contains.
SaGraph: A Similarity-aware Hardware Accelerator for Temporal Graph Processing

Temporal graph processing is used to handle the snapshots of the temporal graph, which concerns changes in graph over time. Although several software/hardware solutions have been designed for efficient temporal graph processing, they still suffer from serious irregular data access due to the uncoordinated graph traversal. To overcome these limitations, this paper proposes SaGraph, a domain-specific hardware accelerator to support the efficient processing of temporal graph. Specifically, temporal graph processing shows strong data access similarity, i.e., most graph accesses of the processing of different snapshots are the same and usually refer to a small fraction of vertices. SaGraph can dynamically coordinate the graph traversals and adaptively cache the vertex states to fully exploit the data access similarity for smaller data access overhead. We implemented and evaluated SaGraph on a Xilinx Alveo U280 FPGA card. Compared with the cutting-edge software and hardware solutions, SaGraph achieves 8.5×-157.3×, 4.2×-16.1× speedups and 34.7×-423.6×, 5.3×-14.7× energy savings, respectively.
SCAGuard: Detection and Classification of Cache Side-Channel Attacks via Attack Behavior Modeling and Similarity Comparison

Cache side-channel attacks (CSCAs), capable of deducing secrets by analyzing timing differences in the shared cache behavior of modern processors, pose a serious security threat. While there are approaches for detecting CSCAs and mitigating information leaks, they either fail to detect and classify new variants or have to impractically update deployed systems (e.g., CPU). In this work, we propose a novel approach, named SCAGuard, to detect and classify CSCAs via attack behavior modeling and similarity comparison. Specifically, we introduce the notion of cache state transition enhanced basic block sequences (CST-BBSes) to model attack behaviors which is able to capture both attack-relevant syntactic code information and semantic cache information. We propose an approach to automatically construct CST-BBS models from binary programs. To detect and classify attacks, we adapt a dynamic time warping algorithm to compare the similarity of CST-BBSes between attack and target programs. We implement our approach in a tool SCAGuard and evaluate it using real-world attacks and diverse benign programs. The results confirm the effectiveness of our approach, compared over existing detection approaches. In particular, SCAGuard significantly outperforms the other detection approaches on new variants.
A Blockchain-Based Federated Random Forest Approach for Power-Related Data Collaborative Analysis

As big data technology rapidly evolves in the power grid, various data-driven tasks have become an integral part of the emerging power information system. Data analytics has expanded from within the smart grid to collaborative analytics across industries. Many structured power-related data and machine learning analysis tasks are kept in the smart grid. However, most of the existing federated learning approaches focus on the deep learning domain, and less on the traditional machine learning domain, urgently needed for federated machine learning research. Besides, collaborative analytics are also undermined by the denial or tampering by unhonest participants. In this paper, we propose a blockchain-based federated random forest algorithm that can be used for federated analysis of structured power-related data. Among them, we design a blockchain-oriented distributed federated random forest aggregation method to aggregate decision trees trained by different participants, building trust between participants through the tamper-evident nature of the blockchain. In addition, we propose a double-layer encryption mechanism to protect the confidentiality of the on-chain data. The reading and aggregation of these data is secured by smart contracts, so that even after the complete random forest is published, an attacker cannot determine which of the decision trees was trained by which user, protecting the data privacy of the participants. We validated the method on two real-world UCI datasets. Compared with random forest models, our method is able to achieve 90% of its accuracy. Experimental results indicate our method is effective.
Detection of abnormal sound in industrial equipment based on classification and outlier calculation

Aiming at the problem that there are many abnormal sound types in industrial equipment and few fault samples can be obtained, a method for detecting abnormal sound of industrial equipment is proposed. Firstly, we focus on two improved neural network models: MobileNetV2 based on classification method and Dense AutoEncoder based on outlier calculation method. Combined with the characteristics of neural network, we propose an improved MFCC feature extraction method. In order to improve the training accuracy, the comprehensive perturbation of the training samples is increased by the data augmentation; finally, the influence of hyperparameters on the training effect is analyzed. Experiments shows that the improved method proposed in this paper achieves an excellent recognition rate.
Comparison of Bayes Theorem and Dempster Shafer Methods for Detection Pests of Mayas Rice Plants

Artificial intelligence, especially expert systems, has been widely applied in agriculture to detect pests and diseases that attack a plant. The Mayas rice plant is local in the East Kalimantan region, which has experienced a decline in production due to pest attacks. However, identifying Mayas rice pests is challenging because the symptoms are similar between one type of pest and another. This study used two methods, namely the Bayes Theorem and Dempster Shafer, to determine the highest level of accuracy and the appropriate method for diagnosing Mayas rice pests. The research data consisted of 32 symptoms and ten pests obtained from experts and 50 test data from expert diagnosis and both ways. Comparison testing using the confusion matrix. The results show that the accuracy value for the Bayes Theorem method is 74% and the Dempster Shafer method is 90%, based on the calculation of the confusion matrix. The accuracy value shows that the Dempster Shafer method has the highest accuracy value and is an appropriate method for diagnosing possible types of pests in Mayas rice.
Automatic Detection of Poor Tone Quality in Classical Guitar Playing Using Deep Anomaly Detection Method

Playing the classical guitar requires techniques to control delicate timbres because of its acoustic properties and the characteristics of its nylon strings. Therefore, a consistent fundamental practice focused on single-note playing is important for improving beginners’ classical guitar skills. However, most existing guitar practice support systems are designed for electric and steel-string acoustic guitars and not classical ones. In this study, we propose a system to improve the fundamental skills of classical guitar players. The system automatically detects sounds with poor tone quality caused by an improper playing technique and provides an objective evaluation of single-note performances. In our proposed method, we treat poor-quality sounds that are caused by mistakes while playing as "anomalies" and employing a semi-supervised anomaly detection technique. This approach reduces the effort required to collect and label poor tone quality data and enables the detection of various types of degraded tone quality. Verification experiments confirmed the high accuracy of the proposed model in detecting poor tone quality in classical guitar playing, demonstrating its potential as an effective practice support for classical guitarists.
Missing Values in RGCCA: Algorithms and Comparisons

Regularized generalized canonical correlation analysis (RGCCA) is a general statistical framework for multiblock data analysis. However, multiblock data often have missing structure, i.e., data in one or more blocks may be completely unobserved for a sample. In this work, several solutions were investigated to properly handle missing data structures within the framework of RGCCA then compared on simulations.
Accelerating DNN Inference with Heterogeneous Multi-DPU Engines

The Deep Learning Processor (DPU) programmable engine released by the official Xilinx Vitis AI toolchain has become one of the commercial off-the-shelf (COTS) solutions for Convolutional Neural Networks (CNNs) inference on Xilinx FPGAs. While modern FPGA devices generally have enough hardware resources to accommodate multi-DPUs simultaneously, the Xilinx toolchain currently only supports the deployment of multiple homogeneous DPUs engines that running independent inference tasks (task-level parallelism). In this work, we demonstrate that deployment of multiple heterogeneous DPU engines makes better resource efficiency for a given FPGA device. Moreover, we show that pipelined execution of a CNN inference task over heterogeneous multi-DPU engines may further improve overall inference throughput with carefully designed CNN layers-to-DPU mapping and scheduling. Finally, for a given CNN model and an FPGA device, we propose a comprehensive framework that automatically determines the optimal heterogeneous DPU deployment, and adaptively chooses the execution scheme between task-level and pipelined parallelism. Compared with the state-of-the-art solution with homogeneous multi-DPU engines and network-level parallelism, the proposed framework shows an average improvement of 13% (up-to 19%) and 6.6% (up-to 10%) on the Xilinx Zynq UltraScale+ MPSoC ZCU104 and ZCU102 platforms, respectively.
A Multimodal Contrastive Pre-training Framework for Automatic Ship Detection in SAR Imagery

Synthetic aperture radar (SAR) imagery has been used as a promising data source for automatic ship detection with its capability of targeting objects in all-day and all-weather conditions. Due to the scarce labeled SAR images, previous research usually adopts optical data-based pre-training model to support SAR ship detectors and has made significant progress. Nonetheless, two fundamental issues persist: (i) SAR images suffer from serious intrinsic speckle noise with less texture features; (ii) Strong class-discriminative biases for imaging perspectives and geometry are introduced in the process of transferring pre-trained model. In this paper, we propose a multimodal contrastive pre-training framework (MCPF) to boost automatic ship detection with both intra- and cross-modal correlations between SAR and optical images. Specifically, MCPF implements multimodal contrastive learning strategy by introducing cross-modal contrastive module (CCM) and intra-modal contrastive module (ICM). CCM aims to pull the matched SAR-Optical pairs together while pushing those non-matched apart by measuring the similarity between latent representations. ICM maximizes agreement between differently augmented views of the same data example within each modality. To the best of our knowledge, MCPF is the first work that takes into account multimodal contrastive representation learning for automatic ship detection. Extensive experiments on benchmark datasets demonstrate that MCPF outperforms the existing methods and achieves the state-of-the-art performance.
IP Protection in TinyML

Tiny machine learning (TinyML) is an essential component of emerging smart microcontrollers (MCUs). However, the protection of the intellectual property (IP) of the model is an increasing concern due to the lack of desktop/server-grade resources on these power-constrained devices. In this paper, we propose STML, a system and algorithm co-design to Secure IP of TinyML on MCUs with ARM TrustZone. Our design jointly optimizes memory utilization and latency while ensuring the security and accuracy of emerging models. We implemented a prototype and benchmarked with 7 models, demonstrating STML reduces 40% of model protection runtime overhead on average.