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| | 2 | == Real time, robust and reliable (R^3) machine learning over wireless networks |
| | 3 | **Group Members: **Akshar Vedantham, Kirthana Ram, Varun Kota\\ |
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| | 5 | **Advisor: **Anand Sarwate \\ |
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| | 8 | == Project Objective |
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| | 10 | As machine learning applications continue to be developed, more and more computationally intense tasks will have to be performed on mobile devices such as phones, cars, and drones. Mobile devices often offload data to the cloud to help execute these applications. However, offloading this process can result in delays and a lack of ''latency''. \\ |
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| | 12 | To reduce latency when working with the cloud, several methods have been proposed. The two that we will be focusing on are called ''split computing'' and ''early exiting''. Our goal will be to construct AI/ML algorithms, implement them on Orbit nodes using split computing and early exiting, and build a documented codebase while evaluating the efficiency of these algorithms. |
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