The Effects of Partitioning Strategies on Energy Consumption in Distributed CNN Inference at The Edge

by   Erqian Tang, et al.

Nowadays, many AI applications utilizing resource-constrained edge devices (e.g., small mobile robots, tiny IoT devices, etc.) require Convolutional Neural Network (CNN) inference on a distributed system at the edge due to limited resources of a single edge device to accommodate and execute a large CNN. There are four main partitioning strategies that can be utilized to partition a large CNN model and perform distributed CNN inference on multiple devices at the edge. However, to the best of our knowledge, no research has been conducted to investigate how these four partitioning strategies affect the energy consumption per edge device. Such an investigation is important because it will reveal the potential of these partitioning strategies to be used effectively for reduction of the per-device energy consumption when a large CNN model is deployed for distributed inference at the edge. Therefore, in this paper, we investigate and compare the per-device energy consumption of CNN model inference at the edge on a distributed system when the four partitioning strategies are utilized. The goal of our investigation and comparison is to find out which partitioning strategies (and under what conditions) have the highest potential to decrease the energy consumption per edge device when CNN inference is performed at the edge on a distributed system.


AutoDiCE: Fully Automated Distributed CNN Inference at the Edge

Deep Learning approaches based on Convolutional Neural Networks (CNNs) a...

Dynamic Distribution of Edge Intelligence at the Node Level for Internet of Things

In this paper, dynamic deployment of Convolutional Neural Network (CNN) ...

Divide and Save: Splitting Workload Among Containers in an Edge Device to Save Energy and Time

The increasing demand for edge computing is leading to a rise in energy ...

DistrEdge: Speeding up Convolutional Neural Network Inference on Distributed Edge Devices

As the number of edge devices with computing resources (e.g., embedded G...

Enabling Incremental Knowledge Transfer for Object Detection at the Edge

Object detection using deep neural networks (DNNs) involves a huge amoun...

Adaptive ResNet Architecture for Distributed Inference in Resource-Constrained IoT Systems

As deep neural networks continue to expand and become more complex, most...

Edge-PRUNE: Flexible Distributed Deep Learning Inference

Collaborative deep learning inference between low-resource endpoint devi...

Please sign up or login with your details

Forgot password? Click here to reset