# How AI Learns to Run Faster on Specific Computer Chips > This patent describes how a smart AI system, called a reinforcement learning agent, trains other AI models to run more efficiently on specific computer hardware by cleverly reducing their size without losing too much accuracy. - **Patent:** US 20250371349 - **Original title:** Methods and apparatus for hardware-aware machine learning model training - **Owner:** Intel - **Status:** Active - **Times cited:** 2 - **Field:** ai_ml, semiconductors, software, consumer_electronics, telecommunications ## What it does The patent details a method for optimizing neural networks to run efficiently on a specific hardware device. It uses a "reinforcement learning agent" (Claim 1) that receives an "embedding state" (Claim 1) representing characteristics of a neural network layer, such as its "kernel size" or "number of weights" (Claim 4). Based on this information, the agent generates "actions" (Claim 1) to reduce the "computational cycles" (Claim 1) needed to execute the neural network. A common action is "pruning weights" (Claim 2) within the layer, determined by a "sparsity ratio" (Claim 2). After the modified neural network runs, the system determines a "reward" (Claim 1) for the agent by checking if the output's "accuracy" (Claim 1) meets a set threshold and if a "target cycle reduction" (Claim 6) was achieved. This reward then helps the agent update its "policy" (Claim 1) to make better optimization decisions in the future. For example, an AI model designed to detect objects in a security camera feed could be optimized this way to run quickly on the camera's embedded processor without missing important events. ## What it does NOT cover - Optimizing neural networks using methods that do not involve a reinforcement learning agent to generate actions. - Training neural networks without specifically considering the reduction of computational cycles on a target hardware device. - Pruning weights in a neural network without evaluating the impact on both accuracy and computational cycles as part of a reward system. - Optimization techniques that focus solely on improving neural network accuracy without also aiming to reduce execution time. - Manual optimization of neural network layers by a human engineer without an automated learning agent. ## The clever bit The innovation lies in using a reinforcement learning agent to intelligently *learn* how to optimize a neural network for a specific hardware's performance, rather than relying on fixed rules or manual tuning. This agent dynamically balances the trade-off between the model's accuracy and its computational cost on the target device. ## Real-world examples 1. AI models running on smartphone processors 2. Machine learning inference on embedded systems in smart home devices 3. Neural networks deployed on edge AI accelerators 4. Computer vision tasks on autonomous vehicle hardware 5. Optimized AI for industrial IoT sensors ## Why it matters As AI models become more complex, they demand significant computing power. This patent addresses the critical challenge of deploying these models on devices with limited resources, like smartphones, drones, or IoT sensors. By automatically making AI models smaller and faster for specific hardware, it enables more widespread and efficient use of artificial intelligence outside of large data centers. This is crucial for real-time applications and reducing energy consumption. ## Frequently asked questions ### What does How AI Learns to Run Faster on Specific Computer Chips cover? This patent describes how a smart AI system, called a reinforcement learning agent, trains other AI models to run more efficiently on specific computer hardware by cleverly reducing their size without losing too much accuracy. ### Who owns patent US 20250371349? This patent is owned by Intel. ### When does this patent expire? This patent is expected to expire on August 21, 2045, when the invention enters the public domain. ### What is patent US 20250371349 cited by? This patent has been cited by 2 later patents that build on its ideas. ### What problem does this patent solve? As AI models become more complex, they demand significant computing power. This patent addresses the critical challenge of deploying these models on devices with limited resources, like smartphones, drones, or IoT sensors. By automatically making AI models smaller and faster for specific hardware, it enables more widespread and efficient use of artificial intelligence outside of large data centers. This is crucial for real-time applications and reducing energy consumption. ### What does this patent NOT cover? Optimizing neural networks using methods that do not involve a reinforcement learning agent to generate actions. **Full plain-English explainer:** https://patentbrief.org/patent/us/20250371349/methods-and-apparatus-for-hardware-aware-machine-learning-model-training **Original patent:** https://patents.google.com/patent/US20250371349 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How to Update AI on Small Devices with Slow Internet](https://patentbrief.org/patent/us/20250363357/systems-and-methods-for-deploying-and-updating-neural-networks-at-the-edge-of-a-) — 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. - [Making Neural Networks Faster by Skipping Unnecessary Calculations](https://patentbrief.org/patent/us/11429864/dlss-deep-learning-super-sampling) — A method to speed up AI training by keeping data sparse, meaning it ignores zeros to save memory and processing power during both forward and backward passes. - [Adapting AI Models to Fit Device Resources](https://patentbrief.org/patent/us/20220383078/data-processing-method-and-related-device) — This patent describes how a computer system can automatically shrink a large artificial intelligence model, specifically a "transformer" type, to fit the available computing power of a phone or other device. - [How to Automatically Expand Neural Networks by Adding New Nodes](https://patentbrief.org/patent/us/10832138/gpt-language-model-pre-training) — A method for growing artificial intelligence models by identifying underperforming parts of a network and adding new nodes based on the behavior of existing ones. - [How to Fix Faulty Memory Cells in AI Chips](https://patentbrief.org/patent/us/10956815/killing-asymmetric-resistive-processing-units-for-neural-network-training) — This patent describes a system that tests individual memory cells in AI chips for uneven behavior and then permanently disables the faulty ones before the chip starts learning, making AI training more efficient.