# Distributed Deep Convolutional Neural Networks for the Internet-of-Things

## TL;DR

This paper presents a methodology for deploying distributed deep convolutional neural networks on IoT devices, optimizing for low latency and resource constraints to enable real-time, autonomous decision-making.

## Contribution

It introduces a formal optimization-based design methodology for allocating CNN execution across IoT units considering memory and processing constraints.

## Key findings

- Optimized CNN distribution reduces decision latency.
- Supports multiple data sources and CNNs simultaneously.
- Enables real-time, autonomous IoT applications.

## Abstract

Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load. In order to support a real-time execution of the considered DL model at the IoT unit level, DL solutions must be designed having in mind constraints on memory and processing capability exposed by the chosen IoT technology. In this paper, we introduce a design methodology aiming at allocating the execution of Convolutional Neural Networks (CNNs) on a distributed IoT application. Such a methodology is formalized as an optimization problem where the latency between the data-gathering phase and the subsequent decision-making one is minimized, within the given constraints on memory and processing load at the units level. The methodology supports multiple sources of data as well as multiple CNNs in execution on the same IoT system allowing the design of CNN-based applications demanding autonomy, low decision-latency, and high Quality-of-Service.