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

arXiv: https://arxiv.org/abs/2210.08392 · HTML: https://mlsanity.com/paper/2210.08392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://mlsanity.com/paper/1803.01164/) (2018)
- [DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework](https://mlsanity.com/paper/1706.01215/) (2017)
- [High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors](https://mlsanity.com/paper/1903.05898/) (2019)
- [Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization](https://mlsanity.com/paper/2105.11013/) (2021)

## Built on by (most cited first, 1 of 1)

- [Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems](https://mlsanity.com/paper/2404.13348/) (2024)

## Abstract (arXiv)

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.