pwc-archive/papers-with-abstracts huggingface dataset:
AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs
Authors: Mingzhe Gao, Jieru Zhao, Zhe Lin, Wenchao Ding, Xiaofeng Hou, Yu Feng, Chao Li, Minyi Guo
Date: 2024-07-21 00:00:00
Tasks: Code Generation, Dataset Generation, RAG, Retrieval-augmented Generation

Recently, the use of large language models (LLMs) for software code generation, e.g., C/C++ and Python, has proven a great success. However, LLMs still suffer from low syntactic and functional correctness when it comes to the generation of register-transfer level (RTL) code, such as Verilog. To address this issue, in this paper, we develop AutoVCoder, a systematic open-source framework that significantly improves the LLMs' correctness of generating Verilog code and enhances the quality of its output at the same time. Our framework integrates three novel techniques, including a high-quality hardware dataset generation approach, a two-round LLM fine-tuning method and a domain-specific retrieval-augmented generation (RAG) mechanism. Experimental results demonstrate that AutoVCoder outperforms both industrial and academic LLMs in Verilog code generation. Specifically, AutoVCoder shows a 0.5% and 2.2% improvement in functional correctness on the EvalMachine and EvalHuman benchmarks compared with BetterV, and also achieves a 3.4% increase in syntax correctness and a 3.4% increase in functional correctness on the RTLLM benchmark compared with RTLCoder.
LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits
Authors: Chen-Chia Chang, Yikang Shen, Shaoze Fan, Jing Li, Shun Zhang, Ningyuan Cao, Yiran Chen, Xin Zhang
Date: 2024-07-19 00:00:00
Tasks: Electrical Engineering, Graph Generation, Language Modeling, Language Modelling

In the realm of electronic and electrical engineering, automation of analog circuit is increasingly vital given the complexity and customized requirements of modern applications. However, existing methods only develop search-based algorithms that require many simulation iterations to design a custom circuit topology, which is usually a time-consuming process. To this end, we introduce LaMAGIC, a pioneering language model-based topology generation model that leverages supervised finetuning for automated analog circuit design. LaMAGIC can efficiently generate an optimized circuit design from the custom specification in a single pass. Our approach involves a meticulous development and analysis of various input and output formulations for circuit. These formulations can ensure canonical representations of circuits and align with the autoregressive nature of LMs to effectively addressing the challenges of representing analog circuits as graphs. The experimental results show that LaMAGIC achieves a success rate of up to 96\% under a strict tolerance of 0.01. We also examine the scalability and adaptability of LaMAGIC, specifically testing its performance on more complex circuits. Our findings reveal the enhanced effectiveness of our adjacency matrix-based circuit formulation with floating-point input, suggesting its suitability for handling intricate circuit designs. This research not only demonstrates the potential of language models in graph generation, but also builds a foundational framework for future explorations in automated analog circuit design.
MCU-MixQ: A HW/SW Co-optimized Mixed-precision Neural Network Design Framework for MCUs
Authors: Junfeng Gong, Cheng Liu, Long Cheng, Huawei Li, Xiaowei Li
Date: 2024-07-17 00:00:00
Tasks: Neural Architecture Search, Quantization

Mixed-precision neural network (MPNN) that utilizes just enough data width for the neural network processing is an effective approach to meet the stringent resources constraints including memory and computing of MCUs. Nevertheless, there is still a lack of sub-byte and mixed-precision SIMD operations in MCU-class ISA and the limited computing capability of MCUs remains underutilized, which further aggravates the computing bound encountered in neural network processing. As a result, the benefits of MPNNs cannot be fully unleashed. In this work, we propose to pack multiple low-bitwidth arithmetic operations within a single instruction multiple data (SIMD) instructions in typical MCUs, and then develop an efficient convolution operator by exploring both the data parallelism and computing parallelism in convolution along with the proposed SIMD packing. Finally, we further leverage Neural Architecture Search (NAS) to build a HW/SW co-designed MPNN design framework, namely MCU-MixQ. This framework can optimize both the MPNN quantization and MPNN implementation efficiency, striking an optimized balance between neural network performance and accuracy. According to our experiment results, MCU-MixQ achieves 2.1$\times$ and 1.4$\times$ speedup over CMix-NN and MCUNet respectively under the same resource constraints.
Latency optimized Deep Neural Networks (DNNs): An Artificial Intelligence approach at the Edge using Multiprocessor System on Chip (MPSoC)
Authors: Seyed Nima Omidsajedi, Rekha Reddy, Jianming Yi, Jan Herbst, Christoph Lipps, Hans Dieter Schotten
Date: 2024-07-16 00:00:00
Tasks: Autonomous Driving, Edge-computing

Almost in every heavily computation-dependent application, from 6G communication systems to autonomous driving platforms, a large portion of computing should be near to the client side. Edge computing (AI at Edge) in mobile devices is one of the optimized approaches for addressing this requirement. Therefore, in this work, the possibilities and challenges of implementing a low-latency and power-optimized smart mobile system are examined. Utilizing Field Programmable Gate Array (FPGA) based solutions at the edge will lead to bandwidth-optimized designs and as a consequence can boost the computational effectiveness at a system-level deadline. Moreover, various performance aspects and implementation feasibilities of Neural Networks (NNs) on both embedded FPGA edge devices (using Xilinx Multiprocessor System on Chip (MPSoC)) and Cloud are discussed throughout this research. The main goal of this work is to demonstrate a hybrid system that uses the deep learning programmable engine developed by Xilinx Inc. as the main component of the hardware accelerator. Then based on this design, an efficient system for mobile edge computing is represented by utilizing an embedded solution.
Classification-Based Automatic HDL Code Generation Using LLMs
Authors: Wenhao Sun, Bing Li, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Ulf Schlichtmann
Date: 2024-07-04 00:00:00
Tasks: Classification, Code Generation, Hallucination

While large language models (LLMs) have demonstrated the ability to generate hardware description language (HDL) code for digital circuits, they still suffer from the hallucination problem, which leads to the generation of incorrect HDL code or misunderstanding of specifications. In this work, we introduce a human-expert-inspired method to mitigate the hallucination of LLMs and improve the performance in HDL code generation. We first let LLMs classify the type of the circuit based on the specifications. Then, according to the type of the circuit, we split the tasks into several sub-procedures, including information extraction and human-like design flow using Electronic Design Automation (EDA) tools. Besides, we also use a search method to mitigate the variation in code generation. Experimental results show that our method can significantly improve the functional correctness of the generated Verilog and reduce the hallucination of LLMs.
Dihedral Angle Adherence: Evaluating Protein Structure Predictions in the Absence of Experimental Data
Authors: Musa Azeem, Homayoun Valafar
Date: 2024-07-09 00:00:00
Tasks: Prediction, Protein Structure Prediction

Determining the 3D structures of proteins is essential in understanding their behavior in the cellular environment. Computational methods of predicting protein structures have advanced, but assessing prediction accuracy remains a challenge. The traditional method, RMSD, relies on experimentally determined structures and lacks insight into improvement areas of predictions. We propose an alternative: analyzing dihedral angles, bypassing the need for the reference structure of an evaluated protein. Our method segments proteins into amino acid subsequences and searches for matches, comparing dihedral angles across numerous proteins to compute a metric using Mahalanobis distance. Evaluated on many predictions, our approach correlates with RMSD and identifies areas for prediction enhancement. This method offers a promising route for accurate protein structure prediction assessment and improvement.
The Structure of Financial Equity Research Reports -- Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4
Authors: Adria Pop, Jan Spörer
Date: 2024-07-04 00:00:00
Tasks: Sentence, Text Generation

This research dissects financial equity research reports (ERRs) by mapping their content into categories. There is insufficient empirical analysis of the questions answered in ERRs. In particular, it is not understood how frequently certain information appears, what information is considered essential, and what information requires human judgment to distill into an ERR. The study analyzes 72 ERRs sentence-by-sentence, classifying their 4940 sentences into 169 unique question archetypes. We did not predefine the questions but derived them solely from the statements in the ERRs. This approach provides an unbiased view of the content of the observed ERRs. Subsequently, we used public corporate reports to classify the questions' potential for automation. Answers were labeled "text-extractable" if the answers to the question were accessible in corporate reports. 78.7% of the questions in ERRs can be automated. Those automatable question consist of 48.2% text-extractable (suited to processing by large language models, LLMs) and 30.5% database-extractable questions. Only 21.3% of questions require human judgment to answer. We empirically validate using Llama-3-70B and GPT-4-turbo-2024-04-09 that recent advances in language generation and information extraction enable the automation of approximately 80% of the statements in ERRs. Surprisingly, the models complement each other's strengths and weaknesses well. The research confirms that the current writing process of ERRs can likely benefit from additional automation, improving quality and efficiency. The research thus allows us to quantify the potential impacts of introducing large language models in the ERR writing process. The full question list, including the archetypes and their frequency, will be made available online after peer review.
MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detection
Authors: Wissal Hamhoum, Soumaya Cherkaoui
Date: 2024-07-26 00:00:00
Tasks: Cloud Detection, Language Modeling, Language Modelling, Large Language Model

Vehicular networks are exposed to various threats resulting from malicious attacks. These threats compromise the security and reliability of communications among road users, thereby jeopardizing road and traffic safety. One of the main vectors of these attacks within vehicular networks is misbehaving vehicles. To address this challenge, we propose deploying a pretrained Large Language Model (LLM)-empowered Misbehavior Detection System (MDS) within an edge-cloud detection framework. Specifically, we fine-tune Mistral-7B, a state-of-the-art LLM, as the edge component to enable real-time detection, whereas a larger LLM deployed in the cloud can conduct a more comprehensive analysis. Our experiments conducted on the extended VeReMi dataset demonstrate Mistral-7B's superior performance, achieving 98\% accuracy compared to other LLMs such as LLAMA2-7B and RoBERTa. Additionally, we investigate the impact of window size on computational costs to optimize deployment efficiency. Leveraging LLMs in MDS shows interesting results in improving the detection of vehicle misbehavior, consequently strengthening vehicular network security to ensure the safety of road users.
Privacy-Preserving Hierarchical Model-Distributed Inference
Authors: Fatemeh Jafarian Dehkordi, Yasaman Keshtkarjahromi, Hulya Seferoglu
Date: 2024-07-25 00:00:00
Tasks: model, Privacy Preserving

This paper focuses on designing a privacy-preserving Machine Learning (ML) inference protocol for a hierarchical setup, where clients own/generate data, model owners (cloud servers) have a pre-trained ML model, and edge servers perform ML inference on clients' data using the cloud server's ML model. Our goal is to speed up ML inference while providing privacy to both data and the ML model. Our approach (i) uses model-distributed inference (model parallelization) at the edge servers and (ii) reduces the amount of communication to/from the cloud server. Our privacy-preserving hierarchical model-distributed inference, privateMDI design uses additive secret sharing and linearly homomorphic encryption to handle linear calculations in the ML inference, and garbled circuit and a novel three-party oblivious transfer are used to handle non-linear functions. privateMDI consists of offline and online phases. We designed these phases in a way that most of the data exchange is done in the offline phase while the communication overhead of the online phase is reduced. In particular, there is no communication to/from the cloud server in the online phase, and the amount of communication between the client and edge servers is minimized. The experimental results demonstrate that privateMDI significantly reduces the ML inference time as compared to the baselines.
Using Bibliometrics to Detect Unconventional Authorship Practices and Examine Their Impact on Global Research Metrics, 2019-2023
Authors: Lokman I. Meho, Elie A. Akl
Date: 2024-07-07 00:00:00

Between 2019 and 2023, sixteen universities increased their research output by over fifteen times the global average, alongside significant changes in authorship dynamics (e.g., decreased first authorship, rise in hyperprolific authors, increased multi-affiliations, and increased authors per publication rate). Using bibliometric methods, this study detected patterns suggesting a reliance on unconventional authorship practices, such as gift, honorary, and sold authorship, to inflate publication metrics. The study underscores the need for reforms by universities, policymakers, funding agencies, ranking agencies, accreditation bodies, scholarly publishers, and researchers to maintain academic integrity and ensure the reliability of global ranking systems.
A Model for Combinatorial Dictionary Learning and Inference
Authors: Avrim Blum, Kavya Ravichandran
Date: 2024-07-26 00:00:00
Tasks: Dictionary Learning, model

We are often interested in decomposing complex, structured data into simple components that explain the data. The linear version of this problem is well-studied as dictionary learning and factor analysis. In this work, we propose a combinatorial model in which to study this question, motivated by the way objects occlude each other in a scene to form an image. First, we identify a property we call "well-structuredness" of a set of low-dimensional components which ensures that no two components in the set are too similar. We show how well-structuredness is sufficient for learning the set of latent components comprising a set of sample instances. We then consider the problem: given a set of components and an instance generated from some unknown subset of them, identify which parts of the instance arise from which components. We consider two variants: (1) determine the minimal number of components required to explain the instance; (2) determine the correct explanation for as many locations as possible. For the latter goal, we also devise a version that is robust to adversarial corruptions, with just a slightly stronger assumption on the components. Finally, we show that the learning problem is computationally infeasible in the absence of any assumptions.
Predicting 3D Rigid Body Dynamics with Deep Residual Network
Authors: Abiodun Finbarrs Oketunji
Date: 2024-07-09 00:00:00
Tasks: Friction, Physics-informed machine learning

This study investigates the application of deep residual networks for predicting the dynamics of interacting three-dimensional rigid bodies. We present a framework combining a 3D physics simulator implemented in C++ with a deep learning model constructed using PyTorch. The simulator generates training data encompassing linear and angular motion, elastic collisions, fluid friction, gravitational effects, and damping. Our deep residual network, consisting of an input layer, multiple residual blocks, and an output layer, is designed to handle the complexities of 3D dynamics. We evaluate the network's performance using a datasetof 10,000 simulated scenarios, each involving 3-5 interacting rigid bodies. The model achieves a mean squared error of 0.015 for position predictions and 0.022 for orientation predictions, representing a 25% improvement over baseline methods. Our results demonstrate the network's ability to capture intricate physical interactions, with particular success in predicting elastic collisions and rotational dynamics. This work significantly contributes to physics-informed machine learning by showcasing the immense potential of deep residual networks in modeling complex 3D physical systems. We discuss our approach's limitations and propose future directions for improving generalization to more diverse object shapes and materials.
Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels
Authors: Zhe Wang, Jiayi Zhang, Emil Björnson, Dusit Niyato, Bo Ai
Date: 2024-07-26 00:00:00

In this paper, we explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading channels. We provide a spectral efficiency (SE) performance analysis framework for both the centralized and distributed processing schemes with bilinear equalizer (BE)-structure combining schemes applied. The BE-structured combining is a set of schemes that are constructed by the multiplications of channel statistics-based BE matrices and instantaneous channel estimates. Notably, we derive closed-form achievable SE expressions for centralized and distributed BE-structured combining schemes. We propose one centralized and two distributed OBE schemes: Centralized OBE (C-OBE), Distributed OBE based on Global channel statistics (DG-OBE), and Distributed OBE based on Local channel statistics (DL-OBE), which maximize their respective SE expressions. OBE matrices in these schemes are tailored based on varying levels of channel statistics. Notably, we obtain new and insightful closed-form results for the C-OBE, DG-OBE, and DL-OBE combining schemes. Numerical results demonstrate that the proposed OBE schemes can achieve excellent SE, even in scenarios with severe pilot contamination.
Atmospheric Density-Compensating Model Predictive Control for Targeted Reentry of Drag-Modulated Spacecraft
Authors: Alex D. Hayes, Ryan J. Caverly
Date: 2024-07-26 00:00:00
Tasks: Model Predictive Control

This paper presents an estimation and control framework that enables the targeted reentry of a drag-modulated spacecraft in the presence of atmospheric density uncertainty. In particular, an extended Kalman filter (EKF) is used to estimate the in-flight density errors relative to the atmospheric density used to generate the nominal guidance trajectory. This information is leveraged within a model predictive control (MPC) strategy to improve tracking performance, reduce control effort, and increase robustness to actuator saturation compared to the state-of-the-art approach. The estimation and control framework is tested in a Monte Carlo simulation campaign with historical space weather data. These simulation efforts demonstrate that the proposed framework is able to stay within 100 km of the guidance trajectory at all points in time for 98.4% of cases. The remaining 1.6% of cases were pushed away from the guidance by large density errors, many due to significant solar storms and flares, that could not physically be compensated for by the drag control device. For the successful cases, the proposed framework was able to guide the spacecraft to the desired location at the entry interface altitude with a mean error of 12.1 km and 99.7% of cases below 100 km.
Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram
Authors: Jingping Nie, Ran Liu, Behrooz Mahasseni, Erdrin Azemi, Vikramjit Mitra
Date: 2024-07-25 00:00:00
Tasks: Heart rate estimation, Multi-Task Learning

Acoustic signals are crucial for health monitoring, particularly heart sounds which provide essential data like heart rate and detect cardiac anomalies such as murmurs. This study utilizes a publicly available phonocardiogram (PCG) dataset to estimate heart rate using model-driven methods and extends the best-performing model to a multi-task learning (MTL) framework for simultaneous heart rate estimation and murmur detection. Heart rate estimates are derived using a sliding window technique on heart sound snippets, analyzed with a combination of acoustic features (Mel spectrogram, cepstral coefficients, power spectral density, root mean square energy). Our findings indicate that a 2D convolutional neural network (\textbf{\texttt{2dCNN}}) is most effective for heart rate estimation, achieving a mean absolute error (MAE) of 1.312 bpm. We systematically investigate the impact of different feature combinations and find that utilizing all four features yields the best results. The MTL model (\textbf{\texttt{2dCNN-MTL}}) achieves accuracy over 95% in murmur detection, surpassing existing models, while maintaining an MAE of 1.636 bpm in heart rate estimation, satisfying the requirements stated by Association for the Advancement of Medical Instrumentation (AAMI).
Online Social Network Data-Driven Early Detection on Short-Form Video Addiction
Authors: Fang-Yu Kuo
Date: 2024-07-24 00:00:00
Tasks: Form

Short-form video (SFV) has become a globally popular form of entertainment in recent years, appearing on major social media platforms. However, current research indicate that short video addiction can lead to numerous negative effects on both physical and psychological health, such as decreased attention span and reduced motivation to learn. Additionally, Short-form Video Addiction (SFVA) has been linked to other issues such as a lack of psychological support in real life, family or academic pressure, and social anxiety. Currently, the detection of SFVA typically occurs only after users experience negative consequences. Therefore, we aim to construct a short video addiction dataset based on social network behavior and design an early detection framework for SFVA. Previous mental health detection research on online social media has mostly focused on detecting depression and suicidal tendency. In this study, we propose the first early detection framework for SFVA EarlySD. We first introduce large language models (LLMs) to address the common issues of sparsity and missing data in graph datasets. Meanwhile, we categorize social network behavior data into different modalities and design a heterogeneous social network structure as the primary basis for detecting SFVA. We conduct a series of quantitative analysis on short video addicts using our self-constructed dataset, and perform extensive experiments to validate the effectiveness of our method EarlySD, using social data and heterogeneous social graphs in the detection of short video addiction.