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Ph.D. Project: A Compiler-Driven Approach to HW/SW Co-Design of Deep-Learning Accelerators 32

Ph.D. Project: A Compiler-Driven Approach to HW/SW Co-Design...

引用

32nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2024

作者： Kamucheka, Tendayi Andrews, David University of Arkansas Department of Electrical Engineering and Computer Science FayettevilleAR72701 United States

ISBN: (纸本)9798350372434

This work introduces SPAR, a compiler framework based on MLIR, tailored for deep learning inference applications. Alongside SPAR, we present SPAR HL, an extensible Instruction Set Architecture (ISA) designed to seamlessly interface custom FPGA-based accelerators with the SPAR compiler. Historically, custom accelerators on FPGA have posed a challenge as a compiler target. Prior compiler initiatives have addressed this issue by generating application-specific hardware during compilation, necessitating expertise across both application and hardware domains. In response, we offer an alternative approach by proposing an ISA as a unified compiler target and hardware interface for custom accelerators. Furthermore, we introduce a compiler capable of translating high-level machine learning models encoded in ONNX into code compatible with our proposed ISA, thus enabling efficient deployment on FPGA-based custom accelerators. © 2024 IEEE.

关键词： Program compilers

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A Bilayered Decomposition Technique for Handling Complex Constrained Multi-Objective Optimization Problems

A Bilayered Decomposition Technique for Handling Complex Con...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yasuda, Yusuke Tamura, Kenichi Yasuda, Keiichiro Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo191-0065 Japan

ISBN: (纸本)9781665410205

Constrained multi-objective optimization problems (CMOPs) are prevalent in real-world applications, dealing with multi-objective and constraint functions. Multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) has proven effective for unconstrained multi-objective optimization problems with complex Pareto front (PF). However, in CMOPs, the conflicting nature between objectives and constraints often makes it challenging to appropriately manage constraints while ensuring convergence, diversity, and feasibility of the solution set towards the PF. To address this issue, this paper proposes a bilayered decomposition technique. The proposed algorithm treats constraints as an additional objective function, separately decomposing the objective space and the constraint violation space. This method allows for the simultaneous attainment of convergence, diversity, and feasibility in the solution set while efficiently exploring the PF. Experimental results demonstrate that, within the MOEA/D-DE framework, our algorithm efficiently navigates complex PFs on challenging problems such as LIR-CMOPs and DAS-CMOPs, matching the performance of state-of-the-art constrained multi-objective evolutionary algorithms. © 2024 IEEE.

关键词： Constrained optimization

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Quantum Computing Circuit Design: A Tutorial 15

Quantum Computing Circuit Design: A Tutorial

引用

15th International Conference on Advanced Scientific Computing, ICASC 2024

作者： Abdul Wahid, Seham Al Asad, Arghavan Kaur, Rupinder Mohammadi, Farah Toronto Metropolitan University Electrical and Computer Engineering Department Toronto Canada School of Computer Science and Technology Algoma University Brampton Canada

ISBN: (纸本)9798350356526

Recent advancements in computing speed and capacity of Artificial Intelligence (AI) algorithms have reached a saturation level in performance due to the continuous application of Moore's law which resulted in the memory wall phenomenon. Quantum computing (QC), based on the principles of quantum mechanics which include superposition and entanglement, has the potential to contribute to the advancements of AI by providing exponential speedup. Quantum computers use quantum bits, also known as qubits, as the metric to demonstrate the power of quantum technology. Qubits, the fundamental unit of quantum information can exist in two states, 0 and 1, simultaneously, leveraging the principle of superposition. Quantum gates are the building blocks of quantum circuits, operating on qubits to transform one quantum state into another, and are comparative to classical logic gates. Designing and simulating quantum circuits can be completed using the Qiskit library design by IBM. Qiskit is a cloud-based, open access platform to real quantum computers with 5 to 16 qubits. This paper offers a straightforward tutorial on implementing one, two and three-qubit quantum gates, as well as designing quantum circuits using the Qiskit library, illustrated with an example of a quantum half adder implementation. © 2024 IEEE.

关键词： Qubits

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Wind energy production using novel HCS algorithm to reach MPPT for small-scale wind turbines under rapid change wind speed

Wind energy production using novel HCS algorithm to reach MP...

引用

International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Badawi, Ahmed Ali, Hassan Ismail, Nasser A. Zyoud, Alhareth Yusoff, Siti Hajar College of Engineering Technology University of Doha for Science and Technology Doha Qatar Department of Electrical and Computer Engineering Birzeit University Birzeit Ramallah Palestine Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781032803722

In this paper, a novel algorithm had been proposed to improve the efficiency performance of the wind energy conversion system (WECS) to reach the maximum possible captured power under fast varying wind speed. The permanent magnet synchronous generator (PMSG) had been selected because of its reliability and efficient performance, especially in low-speed conditions environment. The novel algorithm applied the power prediction mode to make optimization for the reached decision to the maximum peak point. The proposed algorithm shows better efficiency performance for different wind speed conditions. The novel algorithm accelerates convergence to detect the maximum peak point without sacrificing efficiency. The novel algorithm’s simplicity reduces the overall time required by 33% when compared to the three-mode algorithm. © 2025 The Author(s).

关键词： Windmill

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Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks 38

Sequential Signal Mixing Aggregation for Message Passing Gra...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Taraday, Mitchell Keren David, Almog Baskin, Chaim Department of Computer Science Technion Haifa Israel School of Electrical and Computer Engineering Ben-Gurion University of the Negev Be'er Sheva Israel

Message Passing Graph Neural Networks (MPGNNs) have emerged as the preferred method for modeling complex interactions across diverse graph entities. While the theory of such models is well understood, their aggregation module has not received sufficient attention. Sum-based aggregators have solid theoretical foundations regarding their separation capabilities. However, practitioners often prefer using more complex aggregations and mixtures of diverse aggregations. In this work, we unveil a possible explanation for this gap. We claim that sum-based aggregators fail to "mix" features belonging to distinct neighbors, preventing them from succeeding at downstream tasks. To this end, we introduce Sequential Signal Mixing Aggregation (SSMA), a novel plug-and-play aggregation for MPGNNs. SSMA treats the neighbor features as 2D discrete signals and sequentially convolves them, inherently enhancing the ability to mix features attributed to distinct neighbors. By performing extensive experiments, we show that when combining SSMA with well-established MPGNN architectures, we achieve substantial performance gains across various benchmarks, achieving new state-of-the-art results in many settings. We published our code at https://***/SSMA/. © 2024 Neural information processing systems foundation. All rights reserved.

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FAST NONLINEAR VECTOR QUANTILE REGRESSION 11

FAST NONLINEAR VECTOR QUANTILE REGRESSION

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Rosenberg, Aviv A. Vedula, Sanketh Romano, Yaniv Bronstein, Alex M. Department of Computer Science Technion Israel Department of Electrical and Computer Engineering Technion Israel Sibylla United Kingdom

Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable Y given explanatory features X.A limitation of QR is that it is only defined for scalar target variables, due to the formulation of its objective function, and since the notion of quantiles has no standard definition for multivariate ***, vector quantile regression (VQR) was proposed as an extension of QR for vector-valued target variables, thanks to a meaningful generalization of the notion of quantiles to multivariate distributions via optimal *** its elegance, VQR is arguably not applicable in practice due to several limitations: (i) it assumes a linear model for the quantiles of the target Y given the features X;(ii) its exact formulation is intractable even for modestly-sized problems in terms of target dimensions, number of regressed quantile levels, or number of features, and its relaxed dual formulation may violate the monotonicity of the estimated quantiles;(iii) no fast or scalable solvers for VQR currently *** this work we fully address these limitations, namely: (i) We extend VQR to the non-linear case, showing substantial improvement over linear VQR;(ii) We propose vector monotone rearrangement, a method which ensures the quantile functions estimated by VQR are monotone functions;(iii) We provide fast, GPU-accelerated solvers for linear and nonlinear VQR which maintain a fixed memory footprint, and demonstrate that they scale to millions of samples and thousands of quantile levels;(iv) We release an optimized python package of our solvers as to widespread the use of VQR in real-world applications. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Vectors

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Multi-label Classification using Self-Supervised Learning: Addressing Class Inter-Dependency and Data Imbalance 23

Multi-label Classification using Self-Supervised Learning: A...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Mirzaee, Ghazaleh Doretto, Gianfranco Adjeroh, Donald West Virginia University Lane Department of Computer Science & Electrical Engineering WV United States

ISBN: (纸本)9798350374889

Developing multi-label classification models under significant class imbalance, and when annotating data requires expert-level knowledge remains a major challenge. Additionally, interdependency and correlation among labels are common in multi-label problems. In this work, we introduce a novel framework to address these challenges. Our approach extracts robust discriminative features from unlabeled data through self-supervised contrastive learning and uses an adaptive data augmentation mechanism (ACBA) to balance the dataset. Independent binary classifiers are trained for each class, using a new custom Focal Weighted Cross-Entropy (FWCE) loss function to focus on hard-to-classify examples. A correlation learning module then refines predictions by integrating statistical and domain-specific knowledge. Finally, a meta-learner, employing a Gated Recurrent Unit (GRU) and multi-head attention, identifies complex relationships between classes, even for those that rarely occur together. We used the detection of thoracic diseases using chest X-rays, a domain with a major class imbalance and highly associated labels, to validate our approach. Our findings demonstrate the potential of our method to apply to other medical and non-medical imaging scenarios with similar multi-label classification problems. © 2024 IEEE.

关键词： Self-supervised learning

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Hysteresis Activation Function for Efficient Inference 4

Hysteresis Activation Function for Efficient Inference

引用

4th NeurIPS Efficient Natural Language and Speech Processing Workshop

作者： Kimhi, Moshe Kashani, Idan Mendelson, Avi Baskin, Chaim Technion - Israel Institute of Technology Department of Computer Science Israel Ben-Gurion University of the Negev School of Electrical and Computer Engineering Israel

The widely used ReLU is favored for its hardware efficiency, as the implementation at inference is a one bit sign case, yet suffers from issues such as the "dying ReLU" problem, where during training, neurons fail to activate and constantly remain at zero, as highlighted by Lu et al. [16]. Traditional approaches to mitigate this issue often introduce more complex and less hardware-friendly activation functions. In this work, we propose a Hysteresis Rectified Linear Unit (HeLU), an efficient activation function designed to address the "dying ReLU" problem with minimal complexity. Unlike traditional activation functions with fixed thresholds for training and inference, HeLU employs a variable threshold that refines the backpropagation. This refined mechanism allows simpler activation functions to achieve competitive performance comparable to their more complex counterparts without introducing unnecessary complexity or requiring inductive biases. Empirical evaluations demonstrate that HeLU enhances model generalization across diverse datasets, offering a promising solution for efficient and effective inference suitable for a wide range of neural network architectures. © 2024 Proceedings of Machine Learning Research.

关键词： Hysteresis

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Design of PID Droop Control for Fast Power Sharing in Inverter-Based Microgrid

Design of PID Droop Control for Fast Power Sharing in Invert...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Nazeri, Ahmad Ali Friebe, Jens Zacharias, Peter Faculty of Electrical Engineering Computer Science University of Kassel Department of Power Electronics Kassel Germany

ISBN: (纸本)9798350364446

Microgrids (MGs) serve as central interfaces for distributed generation, predominantly employing voltage source inverters (VSIs). These MGs operate in either autonomous or grid-connected modes, facilitating local power generation and storage. However, autonomous MGs, lacking grid support, are susceptible to instability, particularly with multiple generators sharing power. This paper addresses instability challenges in VSI-based MGs through comprehensive system modeling, analysis, and design. A novel approach is proposed, employing a modified virtual inertia PID droop control with virtual impedance, facilitates swift response to frequency and voltage fluctuations, ensuring precise power allocation among paralleled VSIs. Moreover, this paper presents the small signal analysis of the PID droop control and outlines the secondary control design, encompassing DDSRF-PLL, frequency, and voltage restoration loops. System validation is conducted through MATLAB/Simulink modeling and experimental verification on a hardware-in-the-loop (HiL) testbench, confirming the efficacy of the proposed methodologies in low inertia MGs. © 2024 IEEE.

关键词： MATLAB

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A BOND GRAPH BASED OPTIMAL CAUSALITY ASSIGNMENT METHOD FOR MATSAT- A MATLAB STRUCTURAL ANALYSIS TOOLBOX 15

A BOND GRAPH BASED OPTIMAL CAUSALITY ASSIGNMENT METHOD FOR M...

引用

15th International Conference on Bond Graph Modeling and Simulation, ICBGM 2024

作者： Fernandes, Arnold A. Kimball, Jonathan W. Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States

This paper extends the usage of the MATlab Structural Analysis Toolbox (MATSAT) by including an Optimal Causality Assignment (OCA) method as an alternative to the traditional Sequential Causality Assignment Procedure (SCAP). This method will be particularly beneficial for causality assignment on bond graphs with loops/meshes. First the causality assignment problem is converted to a mixed integer linear programming (MILP) problem considering fixed, preferred, and indifferent causalities, along with causality constraints at junctions and transducers. Then, techniques from the MILP literature are applied to solve this problem and results are shown for BG models with and without loops. © 2024 Society for Modeling & Simulation International (SCS)

关键词： MATLAB

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