The document discusses the optimization of split computing in IoT devices by dynamically selecting the optimal splitting layer based on prediction confidence and computational costs. It highlights the early exit technique in deep neural networks for reducing inference time and computational load, particularly in resource-constrained environments. Key variables include total inference time, compression ratio, and costs associated with offloading and computation at the splitting layer.

1. SPLIT COMPUTING IN IOT DEVICES
Jagveer Singh

2. PROBLEM FORMULATION

3. T=Total inference time for Layer where l is from 0...L layer
Cl is compression ratio which is ratio betwen length of
output to input size.
D is datasize of input .
R = Rate of Transmission from head model to tail model

5. RESEARCH PAPER 2
Objective: dynamically select the optimal splitting
layer based on the prediction confidence and the computational and
communication costs.
Early Exits Technique:
- Early exit in the context of deep neural networks (DNNs) refers to a technique where inference can be
terminated at intermediate layers of the network, rather than processing through all the layers.
- This method allows for faster inference times and reduced computational load, particularly beneficial
for resource-constrained devices such as edge devices in split computing

6. Ci is Confidence means maximum Probability among these estimated
probabilities at Split Layer.
=> O is cost of offloading
=> γi is computation cost at Spliting at Ith layear
=> µ is serves as a conversion factor, enabling us to represent the cost in
terms of confidence

8. THANK YOU