How to Update AI on Small Devices with Slow Internet

This patent describes a method for efficiently updating artificial intelligence models on small, internet-connected devices, like smart cameras, by sending only the changes, or 'patches,' instead of the entire updated model, which saves bandwidth.

What it covers

This patent describes a system for keeping neural networks on 'edge devices' up-to-date, especially when those devices have slow internet connections. First, a powerful central computer trains a neural network and sends this initial version to the edge device (Claim 1). The edge device then collects data, uses the neural network, and sends back specific pieces of information, such as parts of the data it collected, internal calculations called 'activations,' or its final 'inference results' (Claim 4). The central computer uses this feedback to create an improved version of the neural network. Instead of sending the entire updated network back, the central computer generates a 'neural network difference model' (a patch) by comparing the new and old networks (Claim 1). This patch identifies only the changes, using techniques like 'layer freezing' or 'weights freezing' based on specific criteria, and is then sent to the edge device (Claim 1). For example, a smart security camera with a slow cellular connection could receive small updates to its object recognition AI without needing to download a massive new software package each time.

What it doesn't cover

• —Does not cover sending the entire updated neural network to the edge device, as it specifically focuses on sending a 'neural network difference model' (patch).
• —Does not cover updating neural networks on edge devices without a centralized site/device performing the initial training and update generation.
• —Does not cover generating the neural network difference model using techniques other than 'layer freezing' or 'weights freezing' with 'minimum size' or 'minimum delta' methods.
• —Does not cover edge devices that have high-bandwidth uplink capability, as the claims specifically address 'low-bandwidth uplink capability'.
• —Does not cover scenarios where the edge device sends back information other than portions of a dataset, activations, or overall inference results.

The clever bit

The truly clever part is sending only the 'difference' or 'patch' of the neural network rather than the entire updated model. This significantly reduces the amount of data that needs to be sent to edge devices, especially those with slow internet, by intelligently identifying and packaging only the changed parts of the AI model.

Why it matters

As more artificial intelligence moves from cloud data centers to local devices like smart home gadgets and industrial sensors, efficiently updating these AI models becomes crucial. This patent addresses a key challenge for 'edge AI' by enabling updates over limited internet connections. It could help ensure that devices deployed in remote areas or with constrained network access can still benefit from continuous AI improvements. This method helps reduce data transfer costs and speeds up the deployment of new AI capabilities to a vast number of devices.

Real-world examples

• 1.AI-powered security cameras in remote locations
• 2.Smart factory sensors monitoring equipment health
• 3.Agricultural robots performing crop analysis
• 4.Autonomous vehicles receiving targeted AI updates
• 5.Smart home devices with local AI processing