# How Projection Neural Networks Speed Up AI Predictions

> A method for making artificial intelligence models faster and more efficient by using fixed, non-trainable projections to simplify complex data before processing.

- **Patent:** US 11544573
- **Original title:** Projection neural networks
- **Owner:** Google LLC
- **Granted:** 2023
- **Status:** Active
- **Times cited:** 1
- **Field:** ai_ml, consumer_electronics, software

## What it does

This patent describes a way to build neural networks that are computationally cheaper to run. Instead of training every single part of the network, the system uses 'projection layers' that transform input data into a simpler, lower-dimensional format using fixed parameters that do not change during training. These fixed projections act like a filter, mapping complex inputs to a finite set of values (like 0 or 1) before the rest of the network processes them. By keeping these projection parameters constant, the system reduces the amount of math required to generate a final prediction, making the model faster and less memory-intensive.

## What it does NOT cover

- Does not cover neural networks where all parameters are updated during the training process.
- Does not cover models that do not use a finite set of values (like binary 0 or 1) for the projection function output.
- Does not cover systems that lack a projection layer as defined by the specific dot-product and thresholding mechanism described.
- Does not cover traditional deep learning architectures that rely solely on standard backpropagation for all layer weights.

## The clever bit

The innovation is using fixed, non-trainable weights for the initial projection step. By freezing these parameters, the network avoids the computational cost of updating them, effectively 'compressing' the input data into a manageable format without losing the essential features needed for a prediction.

## Real-world examples

1. On-device mobile text classification
2. Efficient keyword spotting in voice assistants
3. Real-time recommendation engines on low-power hardware

## Why it matters

As AI models grow in size, they become difficult to run on devices with limited power, like smartphones or IoT sensors. This patent provides a blueprint for 'lightweight' AI that can perform complex tasks without needing massive server-side computing power. It represents a shift toward efficiency-focused architecture design in machine learning.

## Frequently asked questions

### What does How Projection Neural Networks Speed Up AI Predictions cover?

A method for making artificial intelligence models faster and more efficient by using fixed, non-trainable projections to simplify complex data before processing.

### Who owns patent US 11544573?

Google LLC owns this patent, granted in 2023.

### When does this patent expire?

This patent is expected to expire on January 3, 2043, when the invention enters the public domain.

### What is patent US 11544573 cited by?

This patent has been cited by 1 later patents that build on its ideas.

### What problem does this patent solve?

As AI models grow in size, they become difficult to run on devices with limited power, like smartphones or IoT sensors. This patent provides a blueprint for 'lightweight' AI that can perform complex tasks without needing massive server-side computing power. It represents a shift toward efficiency-focused architecture design in machine learning.

### What does this patent NOT cover?

Does not cover neural networks where all parameters are updated during the training process.

**Full plain-English explainer:** https://patentbrief.org/patent/us/11544573/llama-large-language-model-architecture

**Original patent:** https://patents.google.com/patent/US11544573

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_Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._