A Survey of Machine Learning Applied to Computer Architecture Design

Penney, Drew; Chen, Lizhong

doi:10.48550/arxiv.1909.12373

Cited by 5 publications

(4 citation statements)

References 116 publications

(187 reference statements)

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“…These methods encompass the generation of design intent data, the integration of ML/AI, artificial neural networks (ANN) and deep learning in architectural design, and sustainable urban management in terms of energy efficiency, energy consumption, and infrastructure connectivity, as well as the utilization of ML as a tool at the intersection of art and architecture [37,41,43]. Moreover, the analysis of 2D and 3D data in generative design and the application of AI and ML in sustainable living spaces, urban policies and landscape design [41,43,44], and architectural plan generation [45], including the integration of ML into architectural education [14,[46][47][48][49][50][51][52] and conservation of architectural heritage [53], are also important fields of research. In addition, ML methods have been utilized to forecast carbon emissions during the design stage, as well as to generate design choices for building design with regard to comfort and performance [49,54,55].…”

Section: Machine Learning For Wind Estimation In Built Environmentmentioning

confidence: 99%

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Gür,

Karadag

2024

Buildings

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(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies.

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Section: Machine Learning For Wind Estimation In Built Environmentmentioning

confidence: 99%

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Gür,

Karadag

2024

Buildings

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“…In addition to the aforementioned uses, ML has been widely applied to many other computer architecture aspects, including microarchitecture design and energy/power optimization. Penney and Chen provide a comprehensive survey about ML applications to the computer architecture domain [30]. Simulation Acceleration.…”

Section: Related Workmentioning

confidence: 99%

SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning

Li¹,

Pandey²,

Flynn³

et al. 2021

Preprint

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While cycle-accurate simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic problems under investigation. This work describes a concerted effort, where machine learning (ML) is used to accelerate discrete-event simulation. First, an ML-based instruction latency prediction framework that accounts for both static instruction/architecture properties and dynamic execution context is constructed. Then, a GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor, and its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator. Leveraging modern GPUs, the ML-based simulator outperforms traditional simulators significantly.

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“…Intelligent detection technology is widely used in image detection, such as single-object images, multi-object images, small object images, and sub-category images [ 6 ]. Machine learning includes supervised learning, unsupervised learning, and reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model

Shi

et al. 2022

Foods

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As a raw material for beer, barley seeds play a critical role in producing beers with various flavors. Unexcepted mixed varieties of barley seeds make malt quality uncontrollable and can even destroy beer flavors. To ensure the quality and flavor of malts and beers, beer brewers will strictly check the appropriate varieties of barley seeds during the malting process. There are wide varieties of barley seeds with small sizes and similar features. Professionals can visually distinguish these varieties, which can be tedious and time-consuming and have high misjudgment rates. However, biological testing requires professional equipment, reagents, and laboratories, which are expensive. This study aims to build an automatic artificial intelligence detection method to achieve high performance in multi-barley seed datasets. There are nine varieties of barley seeds (CDC Copeland, AC Metcalfe, Hockett, Scarlett, Expedition, AAC Synergy, Celebration, Legacy, and Tradition). We captured images of these original barley seeds using an iPhone 11 Pro. This study used two mixed datasets, including a single-barley seed dataset and a multi-barley seed dataset, to improve the detection accuracy of multi-barley seeds. The multi-barley seed dataset had random amounts and varieties of barley seeds in each image. The single-barley seed dataset had one barley seed in each image. Data augmentation can reduce overfitting and maximize model performance and accuracy. Multi-variety barley seed recognition deploys an efficient data augmentation method to effectively expand the barley dataset. After adjusting the hyperparameters of the networks and analyzing and augmenting the datasets, the YOLOv5 series network was the most effective in training the two barley seed datasets and achieved the highest performance. The YOLOv5x6 network achieved the second highest performance. The mAP (mean Average Precision) of the trained YOLOv5x6 was 97.5%; precision was 98.4%; recall was 98.1%; the average speed of image detection reached 0.024s. YOLOv5x6 only trained the multi-barley seed dataset; the trained performance was greater than that of the YOLOv5 series. The two datasets had 39.5% higher precision, 27.1% higher recall, and 40.1% higher mAP than when just using the original multi-barley seed dataset. The multi-barley seed detection results showed high performance, robustness, and speed. Therefore, malting and brewing industries can assess the original barley seed quality with the assistance of fast, intelligent, and detected multi-barley seed images.

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A Survey of Machine Learning Applied to Computer Architecture Design

Cited by 5 publications

References 116 publications

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Machine Learning for Pedestrian-Level Wind Comfort Analysis

SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning

Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model

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