== Real time, robust and reliable (R3) machine learning over wireless networks
**Group Members: **Akshar Vedantham, Kirthana Ram, Varun Kota\\

**Advisor: **Anand Sarwate \\


== Project Overview

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''. \\

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. 

== Weekly Progress

**Week 1 (5/28 - 5/30)**

- Phones, cars, and other devices will want to start using ML/AI applications 
- Leveraging the cloud to help them with this
- Issues - latency and security  

Possible Solution - **Early Exiting

**Week 2 (6/03 - 6/06)**

- Familiarizing ourselves with Machine Learning concepts, PyTorch, neural network architecture, gradient descent, cost function, weights and biases
- Met with our advisors, learned about their work, and discussed what projects we wanted to work on

**Week 3 (6/10 - 6/13)**

- Created a NN using the MNIST dataset 
- Achieved an overall network accuracy of 98.17%
- Worked on an NN for classifying fashion outfits via image recognition 
- Read several research papers given to us
- Worked with Orbit to familiarize ourselves with communicating between nodes

**Week 4 (6/17 - 6/20)**

- Created an NN using the CIFAR-10 dataset 

**Week 5 (6/24 - 6/27)**

- Compared the mean accuracy and standard deviation for different thresholds 
- Compared the mean number of early exits and standard deviation for different thresholds
- Set up Nvidia CUDA on orbit nodes 
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**Week 6 (7/01 - 7/03)**