An Introductory Course on the Design of IoT Edge Computing Devices

McConnell, Matthew; Loparo, Kenneth A.; Barendt, Nicholas A.

doi:10.18260/1-2--36677

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…Bansal and Kumar [10] describe a state-of-the-art IoT ecosystem that includes edge devices and uses cloud and fog computing. McConnell et al [11] address an introductory course on designing edge computing devices within the IoT frame, while Warden and Situnayake [12] describe detailed implementations of the TensorFlow Lite library within the TinyML environment for AI applications of low-power microcontrollers like Arduino Nano 33 BLE Sense. This work involves a range of edge devices, from low-power (a few mW for TinyML kit) to highpower devices (30 W for Jetson AGX Orin) of various capabilities.…”

Section: Previous Workmentioning

confidence: 99%

Study of Artificial Intelligence Computing Devices for Undergraduate Computer Science and Engineering Labs

Jaksic,

Ansaf

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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This work describes and analyzes a set of state-of-the-art artificial intelligence (AI) hardware kits created for education and research that can be used in undergraduate AI labs. AI cloud-based computing devices and solutions like the Arduino-based Tiny Machine Learning kits or the mobile app by Edge Impulse, Raspberry Pi-based AIY Voice kits by Google, Quad-core Arm Cortex-A53 and Cortex-M4F-based Google Coral Dev Boards, as well as the more powerful Jetson AGX Xavier (512-core NVIDIA Ampere architecture GPU), and Jetson AGX Orin (2048-core NVIDIA Ampere architecture GPU) Developer kits, are compared using published characteristics and direct experiments. The comparison criteria used are (1) ease of setup and first use, (2) learning curve and required prior knowledge, (3) learning community support availability, (4) suitability for undergraduate learning, (5) computational speed, and (6) cost including both the hardware cost and the subscription services cost. Based on the results of the analysis the tested AI computing devices are ranked for use in various levels of undergraduate curricula. The goal is to provide the faculty interested in developing their own AI labs with some guidance in choosing appropriate AI hardware from an experimental perspective.

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Section: Previous Workmentioning

confidence: 99%

Study of Artificial Intelligence Computing Devices for Undergraduate Computer Science and Engineering Labs

Jaksic,

Ansaf

2023 ASEE Annual Conference &Amp; Exposition Proceedings

View full text Add to dashboard Cite

show abstract

An Introductory Course on the Design of IoT Edge Computing Devices

Cited by 1 publication

References 5 publications

Study of Artificial Intelligence Computing Devices for Undergraduate Computer Science and Engineering Labs

Study of Artificial Intelligence Computing Devices for Undergraduate Computer Science and Engineering Labs

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