# Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices

Source: [Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices](<https://www.sota2.com/research/paper/68fa73961e209131cea5d662>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:53:13.696Z

Authors: Adarsh Prasad Behera, Roberto Morabito, Joerg Widmer, Jaya Prakash Champati

Published: 2024-04-08T14:44:49.000Z

## Abstract

The Hierarchical Inference (HI) paradigm employs a tiered processing: the inference from simple data samples are accepted at the end device, while complex data samples are offloaded to the central servers. HI has recently emerged as an effective method for balancing inference accuracy, data processing, transmission throughput, and offloading cost. This approach proves particularly efficient in scenarios involving resource-constrained edge devices, such as IoT sensors and micro controller units (MCUs), tasked with executing tinyML inference. Notably, it outperforms strategies such as local inference execution, inference offloading to edge servers or cloud facilities, and split inference (i.e., inference execution distributed between two endpoints). Building upon the HI paradigm, this work explores different techniques aimed at further optimizing inference task execution. We propose and discuss three distinct HI approaches and evaluate their utility for image classification.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.09424>)

[PDF](<https://arxiv.org/pdf/2406.09424.pdf>)