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arXiv 2023年

作者： Vakili, Shervin Vaziri, Mobin Zarei, Amirhossein Pierre Langlois, J.M. Montréal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada

Multipliers are widely-used arithmetic operators in digital signal processing and machine learning circuits. Due to their relatively high complexity, they can have high latency and be a significant source of power consumption. One strategy to alleviate these limitations is to use approximate computing. This paper thus introduces an original FPGA-based approximate multiplier specifically optimized for machine learning computations. It utilizes dynamically reconfigurable lookup table (LUT) primitives in AMD-Xilinx technology to realize the core part of the computations. The paper provides an in-depth analysis of the hardware architecture, implementation outcomes, and accuracy evaluations of the multiplier proposed in INT8 precision. Implementation results on an AMD-Xilinx Kintex Ultrascale+ FPGA demonstrate remarkable savings of 64% and 67% in LUT utilization for signed multiplication and multiply-and-accumulation configurations, respectively, when compared to the standard Xilinx multiplier core. Accuracy measurements on four popular deep learning (DL) benchmarks indicate a minimal average accuracy decrease of less than 0.29% during post-training deployment, with the maximum reduction staying less than 0.33%. The source code of this work is available on GitHub. © 2023, CC BY-SA.

关键词： Field programmable gate arrays (FPGA)

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Predicting the First Response Latency of Maintainers and Contributors in Pull Requests

arXiv

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arXiv 2023年

作者： Khatoonabadi, Sayed Hassan Abdellatif, Ahmad Costa, Diego Elias Shihab, Emad The Department of Computer Science & Software Engineering Concordia University MontrealQC Canada The Department of Electrical & Software Engineering University of Calgary CalgaryAB Canada

The success of a Pull Request (PR) depends on the responsiveness of the maintainers and the contributor during the review process. Being aware of the expected waiting times can lead to better interactions and managed expectations for both the maintainers and the contributor. In this paper, we propose a machine-learning approach to predict the first response latency of the maintainers following the submission of a PR, and the first response latency of the contributor after receiving the first response from the maintainers. We curate a dataset of 20 large and popular open-source projects on GitHub and extract 21 features to characterize projects, contributors, PRs, and review processes. Using these features, we then evaluate seven types of classifiers to identify the best-performing models. We also conduct permutation feature importance and SHAP analyses to understand the importance and the impact of different features on the predicted response latencies. We find that our CatBoost models are the most effective for predicting the first response latencies of both maintainers and contributors. Compared to a dummy classifier that always returns the majority class, these models achieved an average improvement of 29% in AUC-ROC and 51% in AUC-PR for maintainers, as well as 39% in AUC-ROC and 89% in AUC-PR for contributors across the studied projects. The results indicate that our models can aptly predict the first response latencies using the selected features. We also observe that PRs submitted earlier in the week, containing an average number of commits, and with concise descriptions are more likely to receive faster first responses from the maintainers. Similarly, PRs with a lower first response latency from maintainers, that received the first response of maintainers earlier in the week, and containing an average number of commits tend to receive faster first responses from the contributors. Additionally, contributors with a higher acceptance rate and a history of timel

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Digital Twin and DRL-Driven Semantic Dissemination for 6G Autonomous Driving Service

Digital Twin and DRL-Driven Semantic Dissemination for 6G Au...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yihang Tao Jun Wu Xi Lin Shahid Mumtaz Soumaya Cherkaoui School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Faculty of Science and Engineering Waseda University Japan Department of Engineering Nottingham Trent University UK Department of Computer and Software Engineering Polytechnique Montreal Canada

Data dissemination is critical for 6G autonomous driving (AD) service because of the extensive demand for real-time traffic information. However, the heavier data transmission burden and more stringent requirements of AD service bring challenges for current data dissemination methods. In this paper, we first propose a novel digital twin (DT)-based semantic dissemination architecture to better support 6G AD service. Under this architecture, an energy-efficient semantic communication mechanism is developed to reduce the data dissemination burden while keeping low semantic model update costs. Meanwhile, the DT network is leveraged to disseminate semantic data in parallel with the physical vehicular networks, which alleviates the physical transmission contention and improves the dissemination efficiency. Second, we design a deep-reinforcement-learning (DRL)-driven semantic data dissemination scheme for the proposed architecture, named Proximal-policy-optimization for Digital-twin-aided Data Dissemination (PD3), which seeks the optimal DT transfer and semantic transmission scheduling strategy. Finally, experimental results show that our approach surpasses the state-of-the-art methods by 18.36% lower dissemination delay and 4.51% higher dissemination ratio on average.

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Real-Time Simulation of a Fast Charger Using a Low-Cost FPGA Platform 48

Real-Time Simulation of a Fast Charger Using a Low-Cost FPGA...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Meddah, Karim Chalangar, Hossein Ould-Bachir, Tarek Polytechnique Montréal Motce Laboratory Department of Computer and Software Engineering MontréalQCH3T 1J4 Canada

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Advances in semiconductor technologies bring to the market power electronic converters (PECs) switched at high frequencies, and make their real-time simulation and testing using a hardware-in-loop configuration difficult. Various approaches have been reported in the literature that demonstrate the feasibility of simulating high switching frequency (HSF) PECs in real-time, but require high-end devices. This paper demonstrates that low-cost FPGA platforms can be leveraged for the accurate real-time simulation of HSF of PEC. The proposed platforms make use of a solver that uses state variables from the previous time-point to determine the status of uncontrolled switches. The performance of the proposed approach is assessed using a battery charger test case. The paper demonstrates that the proposed method is latency-and resource-efficient, and achieves a time-step of 32 ns. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Efficient Modeling of the Routing and Spectrum Allocation Problem for Flexgrid Optical Networks 25

Efficient Modeling of the Routing and Spectrum Allocation Pr...

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25th International Conference on Optical Network Design and Modelling, ONDM 2021

作者： Jaumard, Brigitte Nguyen, Quang Anh Concordia University Department of Computer Science and Software Engineering MontrealQC Canada

ISBN: (纸本)9783903176331

While the problem of Routing and Spectrum Allocation (RSA) has been widely studied, very few studies attempt to solve realistic sized instances. Indeed, the state of the art is always below the standard transport capacity of a fiber link with 384 frequency slots, regardless of what the authors consider, heuristics or exact methods with a few exceptions. In this paper, we are interested in reducing the gap between realistic data sets and testbed instances that are often considered, using exact methods. Even if exact methods may fail to solve in reasonable time very large instances, they can, however, output solutions with a very good and proven accuracy. The novelty of this paper is to exploit the observations that optimal solutions contain a very large number of lightpaths associated with shortest paths or k-shortest paths with a small k. We propose an original efficient large-scale optimization model and decomposition algorithm to solve the RSA problem for flexgrid optical networks. It allows the exact or near optimal solution of much larger instances than in the literature. © 2021 IFIP TC6 WG6.10.

关键词： Optimal systems

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MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration

arXiv

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arXiv 2025年

作者： Xu, Yisen Lin, Feng Yang, Jinqiu Chen, Tse-Hsun Tsantalis, Nikolaos SPEAR Lab Concordia University Montreal Canada O-RISA Lab Concordia University Montreal Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

Maintaining and scaling software systems relies heavily on effective code refactoring, yet this process remains labor-intensive, requiring developers to carefully analyze existing codebases and prevent the introduction of new defects. Although recent advancements have leveraged Large Language Models (LLMs) to automate refactoring tasks, current solutions are constrained in scope and lack mechanisms to guarantee code compilability and successful test execution. In this work, we introduce MANTRA, a comprehensive LLM agent-based framework that automates method-level refactoring. MANTRA integrates Context-Aware Retrieval-Augmented Generation, coordinated Multi-Agent Collaboration, and Verbal Reinforcement Learning to emulate human decision-making during refactoring while preserving code correctness and readability. Our empirical study, conducted on 703 instances of "pure refactorings" (i.e., code changes exclusively involving structural improvements), drawn from 10 representative Java projects, covers the six most prevalent refactoring operations. Experimental results demonstrate that MANTRA substantially surpasses a baseline LLM model (RawGPT), achieving an 82.8% success rate (582/703) in producing code that compiles and passes all tests, compared to just 8.7% (61/703) with RawGPT. Moreover, in comparison to IntelliJ’s LLM-powered refactoring tool (EM-Assist), MANTRA exhibits a 50% improvement in generating Extract Method transformations. A usability study involving 37 professional developers further shows that refactorings performed by MANTRAare perceived to be as readable and reusable as human-written code, and in certain cases, even more favorable. These results highlight the practical advantages of MANTRA and emphasize the growing potential of LLM-based systems in advancing the automation of software refactoring tasks. © 2025, CC BY.

关键词： Decision making

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All-Optical Reconfigurable Activation Function Based on Saturable Absorption

All-Optical Reconfigurable Activation Function Based on Satu...

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IEEE International Symposium on Quality Electronic Design

作者： Oceane Destras Felipe Gohring De Magalhaes Sébastien Le Beux Gabriela Nicolescu Département de génie informatique et génie logiciel Polytechnique Montreal Montreal QC Canada Department of Electrical and Computer Engineering Concordia University Montreal QC Canada

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

The development of optical neural networks (ONNs) has opened up new possibilities for high-speed and energy-efficient computing. However, the limited range of available optical activation functions poses a significant bottleneck. To address this, we propose an all-optical, reconfigurable activation function circuit based on semiconductor saturable absorber mirrors and Mach-Zehnder interferometers, capable of emulating activation functions such as ReLU, Sigmoid, and Tanh. The reprogrammability of the proposed design allows the same circuit to be reused for multiple tasks by dynamically adjusting the activation function, optimizing both space and energy efficiency in ONNs. Through extensive testing, we have identified the most critical parameters of the optical activation function for effective training of ONNs. We demonstrate via numerical simulation that this optical activation function achieves competitive results for the performance of ONNs, reaching up to 98.5% accuracy on the MNIST dataset.

关键词： Integrated optics Training Optical interferometry Optical design Neural networks Optical computing computer architecture Optical fiber networks Nonlinear optics High-speed optical techniques

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Vim4Path: Self-Supervised Vision Mamba for Histopathology Images

Vim4Path: Self-Supervised Vision Mamba for Histopathology Im...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ali Nasiri-Sarvi Vincent Quoc-Huy Trinh Hassan Rivaz Mahdi S. Hosseini Department of Computer Science and Software Engineering (CSSE) Concordia University Canada Institute for Research in Immunology and Cancer of the University of Montreal Canada Department of Electrical and Computer Engineering (ECE) Concordia University Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the Vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against Vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim’s enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim’s capabilities, which reveals that Vim uniquely emulates the pathologist workflow—unlike ViT. This alignment with human expert analysis highlights Vim’s potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at https://***/AtlasAnalyticsLab/Vim4Path.

关键词： Representation learning Training computer vision Adaptation models Computational modeling Self-supervised learning computer architecture

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Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

Electrocoagulation-based AZO DYE (P4R) Removal Rate Predicti...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Akoulih, Meryem Tigani, Smail Byoud, Fouzia Rharib, Meryem El Saadane, Rachid Pierre, Samuel Chehri, Abdellah Ghachtouli, Sanae El IME Lab Faculty of Science Hassan 2 University Casablanca20250 Morocco Research and Development Unit Accsellium LLC Fez Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Computer and Software Engineering Polytechnique Montréal MontréalQC Canada Department of Mathematics and Computer Science Royal Military College of Canada KingstonON11 K7K 7B4 Canada

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Azo dyes

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Evaluating Propagation Models for IIoT in Underground Mining: an Experimental Comparative Study in Underground Coal Mines

Evaluating Propagation Models for IIoT in Underground Mining...

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IEEE ANDESCON

作者： Fabian Medina Oscar Montañez Cristian Suancha Eduardo Avendaño Sandra Céspedes Systems and Computing Engineering school UPTC Sogamoso Colombia Electronic Engineering school UPTC Sogamoso Colombia Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798350355284

ISBN: (纸本)9798350355291

Planning the Industrial Internet of Things (IIoT) systems for underground mining is critical in guaranteeing communication between nodes (sensor/actuator) and ensuring the effectiveness of their functionalities. Therefore, it is necessary to understand how electromagnetic waves disperse in under- ground environments through models that attempt to reproduce their behavior. This understanding is critical to designing and simulating efficient communications for IIoT systems requiring reliable and continuous data transmission. This paper evaluates two propagation models for underground mining tunnels and emphasizes the analysis of the materials that make up the side walls, the floor, and the roof. The analysis of the models is compared with experimental measurements of received power in the 433 MHz band made in coal mines in Colombia. The results, which have direct practical implications, show that the True Rays model generates a behavior similar to that observed in the experimental measurements, with determination coefficients above 74%. At the same time, we assessed the impact of the standard deviation of surface roughness on the accuracy of the received power predictions in the True Rays model.

关键词： Analytical models Accuracy Predictive models Surface roughness Coal mining Rough surfaces Standards Floors Tuning Industrial Internet of Things

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