Towards Energy-Efficient Hyperspectral Image Processing Inside Camera Pixels

Datta, Gourav; Yin, Zihan; Jacob, Ajey; Jaiswal, Akhilesh; Beerel, Peter A.

doi:10.1007/978-3-031-25075-0_22

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…HSI pictures, on the other hand, are often high-dimensional and need a significant amount of power and storage. To address these issues, the authors of the research [87] offer an algorithm-hardware codesign solution for 2D TinyML workloads. They propose inserting sophisticated calculations like convolutions and non-linear activation functions inside and the periphery pixel array.…”

Section: Lithosphere-related Applicationsmentioning

confidence: 99%

“…This decreases the need for data transfer between the image sensor and the accelerator for Convolutional Neural Networks (CNN) processing, resulting in lower energy and bandwidth requirements. The authors in [87] also offer two CNN models that are implemented using PIP (pixel-in-pixel), which yields considerable compression while reducing data rates and power consumption.…”

Section: Lithosphere-related Applicationsmentioning

confidence: 99%

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A Comprehensive Survey on TinyML

Abadade,

Temouden,

Bamoumen

et al. 2023

IEEE Access

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Recent spectacular progress in computational technologies has led to an unprecedented boom in the field of Artificial Intelligence (AI). AI is now used in a plethora of research areas and has demonstrated its capability to bring new approaches and solutions to various research problems. However, the extensive computation required to train AI algorithms comes with a cost. Driven by the need to reduce the energy consumption, the carbon footprint and the cost of computers running machine learning algorithms, TinyML is nowadays considered as a promising AI alternative focusing on technologies and applications for extremely low-profile devices. This paper presents the results of a literature survey of all TinyML applications and related research efforts. Our survey builds a taxonomy of TinyML techniques that have been used so far to bring new solutions to various domains, such as healthcare, smart farming, environment, and anomaly detection. Finally, this survey highlights the remaining challenges and points out possible future research directions. We anticipate that this survey will motivate further discussions on the various fields of applications of TinyML and the synergy of resource-constrained devices and edge intelligence.

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Section: Lithosphere-related Applicationsmentioning

confidence: 99%

Section: Lithosphere-related Applicationsmentioning

confidence: 99%