Performance and Efficiency Evaluation of ASR Inference on the Edge

Gondi, Santosh; Pratap, Vineel

doi:10.3390/su132212392

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…Typically, publications in this topic discuss the problem of deploying AI models in a real scenario or in a scenario with peculiar constraints that challenge a standard approach. For example, deployment publications showcase the challenges of deploying energy‐efficient AI in FPGA (Tao et al, 2020), in Edge devices (Gondi & Pratap, 2021; Kim & Wu, 2020), and in mobile devices (Jayakodi et al, 2020; Manasi et al, 2020; Wang et al, 2022), and so on.…”

Section: Resultsmentioning

confidence: 99%

A systematic review of Green AI

Verdecchia

Sallou

Cruz

2023

WIREs Data Min & Knowl

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With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this article, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm‐agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice. This article is categorized under: Technologies > Machine Learning

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Section: Resultsmentioning

confidence: 99%

A systematic review of Green AI

Verdecchia

Sallou

Cruz

2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Typically, publications in this topic discuss the problem of deploying AI models in a real scenario or in a scenario with peculiar constraints that challenge a standard approach. For example, deployment publications showcase the challenges of deploying energy-efficient AI in FPGA [97], in Edge devices [47,64], in mobile devices [58,75,100], and so on.…”

Section: Green Ai Topicsmentioning

confidence: 99%

“…directly reference the number of data points used. By inspecting such numbers, we note that the number of data points used to study and to evaluate Green AI algorithms and approaches varies greatly, and ranges from 1k data points [47] to 40M data points [41]. Almost half of the studies reporting the number of data points (⊲ 25 out of 48 papers) utilize data points in the order of thousands (1𝑘 ≤ #𝑑𝑎𝑡𝑎𝑝𝑜𝑖𝑛𝑡𝑠 ≤ 70𝑘), while the remaining (⊲ 23 out of 48 papers) use one million data points or more (1𝑀 ≤ #𝑑𝑎𝑡𝑎𝑝𝑜𝑖𝑛𝑡𝑠 ≤ 40𝑀).…”

Section: Dataset Sizesmentioning

confidence: 99%

A Systematic Review of Green AI

Verdecchia¹,

Sallou²,

Cruz³

2023

Preprint

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With the ever-growing adoption of AI-based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this paper, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm-agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice.

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“…The integration of speech recognition can be done using multiple approaches on the Raspberry Pi and NVIDIA Jetson Nano for robots, mobile phones, or any smart device Gondi (2022) .…”

Section: Integration With Embedded Systemsmentioning

confidence: 99%

End-to-end Jordanian dialect speech-to-text self-supervised learning framework

2022

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Speech-to-text engines are extremely needed nowadays for different applications, representing an essential enabler in human–robot interaction. Still, some languages suffer from the lack of labeled speech data, especially in the Arabic dialects or any low-resource languages. The need for a self-supervised training process and self-training using noisy training is proven to be one of the up-and-coming feasible solutions. This article proposes an end-to-end, transformers-based model with a framework for low-resource languages. In addition, the framework incorporates customized audio-to-text processing algorithms to achieve a highly efficient Jordanian Arabic dialect speech-to-text system. The proposed framework enables ingesting data from many sources, making the ground truth from external sources possible by speeding up the manual annotation process. The framework allows the training process using noisy student training and self-supervised learning to utilize the unlabeled data in both pre- and post-training stages and incorporate multiple types of data augmentation. The proposed self-training approach outperforms the fine-tuned Wav2Vec model by 5% in terms of word error rate reduction. The outcome of this work provides the research community with a Jordanian-spoken data set along with an end-to-end approach to deal with low-resource languages. This is done by utilizing the power of the pretraining, post-training, and injecting noisy labeled and augmented data with minimal human intervention. It enables the development of new applications in the field of Arabic language speech-to-text area like the question-answering systems and intelligent control systems, and it will add human-like perception and hearing sensors to intelligent robots.

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Performance and Efficiency Evaluation of ASR Inference on the Edge

Cited by 7 publications

References 21 publications

A systematic review of Green AI

A systematic review of Green AI

A Systematic Review of Green AI

End-to-end Jordanian dialect speech-to-text self-supervised learning framework

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