FPGA Architecture To Enhance Hardware Acceleration for Machine Learning Applications

Itagi, Anirudh; Krishvadana, S; Bharath, Karthik; M, Rajesh Kumar

doi:10.1109/iccmc51019.2021.9418015

Cited by 3 publications

(1 citation statement)

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“…This parallel architecture is very compatible with ML applications, such as neural networks whose training and validation depend largely on the execution of numerous matrix multiplication calculations (Hwang, 2018). More recently, FPGAs (Field Programmable Gate Array) and ASICs (Application Specific Integrated Circuit) have also considerably increased the popularity of ML applications thanks to their ability to support massive parallel computation with a much lower power consumption (Itagi et al, 2021). To facilitate the use of the embedded hardware for processing ML applications, the largest companies in the IT industry, such as Microsoft Azure, Google Cloud, Amazon AWS, IBM Watson and DataRobot, have created several deployment platforms.…”

Section: Machine Learning Workflow In River Research: Opportunities A...mentioning

confidence: 99%

Machine learning applications in river research: Trends, opportunities and challenges

Goethals

2022

Methods Ecol Evol

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1. As one of the earth's key ecosystems, rivers have been intensively studied and modelled through the application of machine learning (ML). With the amount of large data available, these computer algorithms are ever increasing in numerous fields, although there is ongoing scepticism and scholars still question the actual impact and deliverables of algorithms.2. This study aims to provide a systematic review of the state-of-the-art ML-based techniques, trends, opportunities and challenges in river research by applying text mining and automated content analysis.3. Unsupervised and supervised learning have dominated river research while neural networks and deep learning have also gradually gained popularity. Matrix factorisation and linear models have been the most popular ML algorithms, with around 1300 and 800 publications on these topics in 2020 respectively. In contrast, river researchers have had few applications in multiclass and multilabel algorithm, associate rule and Naïve Bayes. 4. The current article proposes an end-to-end workflow of ML applications in river research in order to tackle major ML challenges, including four steps: (1) data collection and preparation; (2) model evaluation and selection; (3) model application; and (4) feedback loops. Within this workflow, river modellers have to balance numerous trade-offs related to model traits, such as complexity, accuracy, interpretability, bias, data privacy and accessibility and spatial and temporal scales. Any choices made when balancing the trade-offs can lead to different model outcomes affecting the final applications. Hence, it is necessary to carefully consider and specify modelling goals, understand the data collected and maintain feedback loops in order to continuously improve model performance and eventually reach the research objectives. Moreover, it remains crucial to address the users' needs and demands that often entail additional elements, such as computational cost, development time and the quantity, quality and compatibility of data. Furthermore, river researchers should account for new technologies and regulations in data collection and protection that are transforming the development and applications of ML, most notably data warehouse and

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Section: Machine Learning Workflow In River Research: Opportunities A...mentioning

confidence: 99%