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Multifunctional Spin Logic Operations in Graphene Spin Circuits

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Physical Review Applied 2022年第6期18卷 064063-064063页

作者： Dmitrii Khokhriakov Shehrin Sayed Anamul Md. Hoque Bogdan Karpiak Bing Zhao Supriyo Datta Saroj P. Dash Department of Microtechnology and Nanoscience Chalmers University of Technology SE-41296 Göteborg Sweden Department of Electrical Engineering and Computer Sciences University of California-Berkeley Berkeley California 94720 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Graphene Center Chalmers University of Technology SE-41296 Göteborg Sweden

Spin-based computing, combining logic and nonvolatile magnetic memory, is promising for emerging information technologies. However, the realization of a universal spin logic operation, representing a reconfigurable building block with all-electrical spin-current communication, has so far remained challenging. Here, we experimentally demonstrate reprogrammable all-electrical multifunctional spin logic operations in a nanoelectronic device architecture, utilizing graphene buses for spin communication and mixing and nanomagnets for writing and reading information at room temperature. This device realizes a multistate spin-majority logic operation, which is reconfigured to achieve (n)and, (n)or, and xnor Boolean operations, depending on the magnetization of inputs. The results are in good agreement with the predictions from a spin-circuit model, providing an experimental demonstration of a spin-based logic unit that takes advantage of the vector nature of spin, as opposed to conventional scalar charge-based devices. These spin logic operations in large-area graphene are fully compatible with industrial fabrication processes and represent a promising platform for scalable all-electric spin-based logic-in-memory computing architecture.

关键词： Magnetotransport Spin current Devices for digital logic, storage & processing Graphene Nanotechnology

Noise reduction optimization of sound sensor based on a Conditional Generation Adversarial Network 2

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Noise reduction optimization of sound sensor based on a Cond...

2021 2nd International Workshop on Electronic communication and Artificial Intelligence, IWECAI 2021

作者： Lin, Xiongwei Yang, Dongru Mao, Yadong Zhou, Lei Zhao, Xiaobo Lu, Shengguo Guangdong Provincial Research Center on Smart Materials Energy Conversion Devices School of Materials and Energy Guangdong University of Technology Guangzhou510006 China School of Computer Science and Engineering Chalmers University of Technology Gothenburg405 30 Sweden

To address the problems in the traditional speech signal noise elimination methods, such as the residual noise, poor real-time performance and narrow applications a new method is proposed to eliminate network voice noise based on deep learning of conditional generation adversarial network. In terms of the perceptual evaluation of speech quality (PESQ) and shorttime objective intelligibility measure (STOI) functions used as the loss function in the neural network, which were used as the loss function in the neural network, the flexibility of the whole network was optimized, and the training process of the model simplified. The experimental results indicate that, under the noisy environment, especially in a restaurant, the proposed noise reduction scheme improves the STOI score by 26.23% and PESQ score by 17.18%, respectively, compared with the traditional Wiener noise reduction algorithm. Therefore, the sound sensor's noise reduction scheme through our approach has achieved a remarkable noise reduction effect, more useful information transmission, and stronger practicability. © Published under licence by IOP Publishing Ltd.

关键词： Speech intelligibility

Metric Dimension and Geodetic Set Parameterized by Vertex Cover

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arXiv 2024年

作者： Foucaud, Florent Galby, Esther Khazaliya, Liana Li, Shaohua Inerney, Fionn Mc Sharma, Roohani Tale, Prafullkumar Université Clermont Auvergne CNRS Mines Saint-Étienne Clermont Auvergne INP LIMOS Clermont-Ferrand63000 France Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden Technische Universität Wien Vienna Austria School of Computer Science and Engineering Central South University Changsha China Telefónica Scientific Research Barcelona Spain University of Bergen Bergen Norway Indian Institute of Science Education and Research Pune Pune India

For a graph G, a subset S ⊆ V (G) is called a resolving set of G if, for any two vertices u, v ∈ V (G), there exists a vertex w ∈ S such that d(w, u) ≠ d(w, v). The Metric Dimension problem takes as input a graph G on n vertices and a positive integer k, and asks whether there exists a resolving set of size at most k. In another metric-based graph problem, Geodetic Set, the input is a graph G and an integer k, and the objective is to determine whether there exists a subset S ⊆ V (G) of size at most k such that, for any vertex u ∈ V (G), there are two vertices s1, s2 ∈ S such that u lies on a shortest path from s1 to s2. These two classical problems turn out to be intractable with respect to the natural parameter, i.e., the solution size, as well as most structural parameters, including the feedback vertex set number and pathwidth. Some of the very few existing tractable results state that they are both FPT with respect to the vertex cover number vc. More precisely, we observe that both problems admit an FPT algorithm running in 2O(vc2) · nO(1) time, and a kernelization algorithm that outputs a kernel with 2O(vc) vertices. We prove that unless the Exponential Time Hypothesis (ETH) fails, Metric Dimension and Geodetic Set, even on graphs of bounded diameter, do not admit an FPT algorithm running in 2o(vc2) · nO(1) time, nor a kernelization algorithm that does not increase the solution size and outputs a kernel with 2o(vc) vertices. The versatility of our technique enables us to apply it to both these problems. We only know of one other problem in the literature that admits such a tight lower bound. Similarly, the list of known problems with exponential lower bounds on the number of vertices in kernelized instances is very short. © 2024, CC BY.

关键词： Geodesy

Rate-Limited Closed-Loop Distributed ISAC Systems: An Autoencoder Approach

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arXiv 2025年

作者： Pan, Guangjin Li, Zhixing Özçelikkale, Ayça Häger, Christian Keskin, Musa Furkan Wymeersch, Henk Department of Electrical Engineering Chalmers University of Technology Sweden Department of Computer Science and Engineering Chalmers University of Technology Sweden Department of Electrical Engineering Uppsala University Sweden

In closed-loop distributed multi-sensor integrated sensing and communication (ISAC) systems, performance often hinges on transmitting high-dimensional sensor observations over rate-limited networks. In this paper, we first present a general framework for rate-limited closed-loop distributed ISAC systems, and then propose an autoencoder-based observation compression method to overcome the constraints imposed by limited transmission capacity. Building on this framework, we conduct a case study using a closed-loop linear quadratic regulator (LQR) system to analyze how the interplay among observation, compression, and state dimensions affects reconstruction accuracy, state estimation error, and control performance. In multi-sensor scenarios, our results further show that optimal resource allocation initially prioritizes low-noise sensors until the compression becomes lossless, after which resources are reallocated to high-noise sensors. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

Utilizing Reinforcement Learning for de novo Drug Design

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arXiv 2023年

作者： Svensson, Hampus Gummesson Tyrchan, Christian Engkvist, Ola Chehreghani, Morteza Haghir Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden Molecular AI Discovery Sciences R&D AstraZeneca Gothenburg Sweden BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden

Deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. Recent studies have demonstrated promising performance for string-based generation of novel molecules utilizing reinforcement learning. In this paper, we develop a unified framework for using reinforcement learning for de novo drug design, wherein we systematically study various on- and off-policy reinforcement learning algorithms and replay buffers to learn an RNN-based policy to generate novel molecules predicted to be active against the dopamine receptor DRD2. Our findings suggest that it is advantageous to use at least both top-scoring and low-scoring molecules for updating the policy when structural diversity is essential. Using all generated molecules at an iteration seems to enhance performance stability for on-policy algorithms. In addition, when replaying high, intermediate, and low-scoring molecules, off-policy algorithms display the potential of improving the structural diversity and number of active molecules generated, but possibly at the cost of a longer exploration phase. Our work provides an open-source framework enabling researchers to investigate various reinforcement learning methods for de novo drug design. © 2023, CC BY.

关键词： Reinforcement learning

Structuring and presenting data for testing of automotive electronics to reduce effort during decision making

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arXiv 2021年

作者： Tran, Martin Husain, Ashish M Department of Computer Science and Engineering Chalmers University of Technology Gothenburg2020 Sweden

Automotive engineering is recognized as a combination of software and mechanical engineering due to the ever-increasing number of software-based components in vehicles. Since vehicles have become more sophisticated than before to ensure robustness, testing of automotive electronics is performed in high volume, producing immense test-related data. This study investigates how unstructured and decentralized test-related data from testing of automotive electronics creates issues in decision making during the testing and analysis process of test artifacts by performing an exploratory case-study at one of the leading automotive companies, Volvo Cars. From the findings of the exploratory study, a prototype was designed to improve the data and information structure and presentation for test analysis and diagnostics for automotive electronics. The prototype's results showed that providing better data and information structure significantly increases the efficiency and reduces the workload for testers when conducting test analysis and diagnostics. Testers showed a decrease in task load for tasks related to testing due to better information structure, presentation, correctness and accessibility. Hence, the improvements aided the testers to arrive at decisions regarding root cause analysis of failed tests efficiently. The findings of this study can assist automotive companies in systematically investigating and improving the testing process of automotive electronics in regards to managing and structuring test-related data. © 2021, CC BY.

关键词： Decision making

Pattern Recognition Method for Detecting engineering Errors on Technical Drawings

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Pattern Recognition Method for Detecting Engineering Errors ...

AI IoT Congress (AIIoT), World

作者： Rimma Dzhusupova Richa Banotra Jan Bosch Helena Holmströ m Olsson Department of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Instrumentation Control &#x0026 Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

Many organizations are looking for how to automate repetitive tasks to reduce manual work and free up resources for innovation. Machine Learning, especially Deep Learning, increases the chance of achieving this goal while working with technical documentation. Highly costly engineering hours can be saved, for example, by empowering the manual check with AI, which helps to reduce the total time for technical documents review. This paper proposes a way to substantially reduce the hours spent by process engineers reviewing P&IDs (Piping & Instrumentation Diagrams). The developed solution is based on a deep learning model for analyzing complex real-life engineering diagrams to find design errors - patterns that are combinations of high-level objects. Through the research on an extensive collection of P&ID files provided by McDermott, we prove that our model recognizes patterns representing engineering mistakes with high accuracy. We also describe our experience dealing with class-imbalance problems, labelling, and model architecture selection. The developed model is domain agnostic and can be re-trained on various schematic diagrams within engineering fields and, as well, could be used as an idea for other researchers to see whether similar solutions could be built for different industries.

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Shisha: Online scheduling of CNN pipelines on heterogeneous architectures

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arXiv 2022年

作者： Soomro, Pirah Noor Abduljabbar, Mustafa Castrillon, Jeronimo Pericàs, Miquel Dept Computer Science and Engineering Chalmers University of Technology Sweden Ohio State University United States Compiler Construction Technische Universität Dresden Germany

Many modern multicore processors integrate asymmetric core clusters. With the trend towards Multi-Chip-Modules (MCMs) and interposer-based packaging technologies, platforms will feature heterogeneity at the level of cores, memory subsystem and the interconnect. Due to their potential high memory throughput and energy efficient core modules, these platforms are prominent targets for emerging machine learning applications, such as Convolutional Neural Networks (CNNs). To exploit and adapt to the diversity of modern heterogeneous chips, CNNs need to be quickly optimized in terms of scheduling and workload distribution among computing resources. To address this we propose Shisha, an online approach to generate and schedule parallel CNN pipelines on heterogeneous MCM-based architectures. Shisha targets heterogeneity in compute performance and memory bandwidth and tunes the pipeline schedule through a fast online exploration technique. We compare Shisha with Simulated Annealing, Hill Climbing and Pipe-Search. On average, the convergence time is improved by ∼ 35× in Shisha compared to other exploration algorithms. Despite the quick exploration, Shisha's solution is often better than that of other heuristic exploration algorithms. © 2022, CC BY-SA.

关键词： Network architecture

Consistent lock-free parallel stochastic gradient descent for fast and stable convergence

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arXiv 2021年

作者： Bäckström, Karl Walulya, Ivan Papatriantafilou, Marina Tsigas, Philippas Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

Stochastic gradient descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including synchronization-free algorithms, e.g. HOGWILD!, have received interest in certain contexts, due to reduced overhead compared to synchronous parallelization. Despite that they induce staleness and inconsistency, they have shown speedup for problems satisfying smooth, strongly convex targets, and gradient sparsity. Recent works take important steps towards understanding the potential of parallel SGD for problems not conforming to these strong assumptions, in particular for deep learning (DL). There is however a gap in current literature in understanding when AsyncSGD algorithms are useful in practice, and in particular how mechanisms for synchronization and consistency play a role. We contribute with answering questions in this gap by studying a spectrum of parallel algorithmic implementations of AsyncSGD, aiming to understand how shared-data synchronization influences the convergence properties in fundamental DL applications. We focus on the impact of consistency-preserving non-blocking synchronization in SGD convergence, and in sensitivity to hyper-parameter tuning. We propose Leashed-SGD, an extensible algorithmic framework of consistency-preserving implementations of AsyncSGD, employing lock-free synchronization, effectively balancing throughput and latency. Leashed-SGD features a natural contention-regulating mechanism, as well as dynamic memory management, allocating space only when needed. We argue analytically about the dynamics of the algorithms, memory consumption, the threads’ progress over time, and the expected contention. The analysis further shows the contention-regulating mechanism that Leashed-SGD enables. We provide a comprehensive empirical evaluation, validating the analytical claims, benchmarking the proposed Leashed-SGD framework, and comparing to baselines for two prominent deep learning (DL

关键词： Gradient methods