# How AI Automatically Builds Efficient Neural Networks for Limited Devices > Google's patent describes a method for automatically designing efficient neural networks by optimizing their internal structure, called 'computational cells,' to meet specific limits on resources like memory or processing speed. - **Patent:** US 20220414425 - **Original title:** Resource constrained neural network architecture search - **Owner:** Google - **Status:** Active - **Times cited:** 2 - **Field:** software, ai_ml, consumer_electronics, telecommunications, semiconductors ## What it does This patent details a computer-implemented method for automatically designing neural networks that are efficient for specific tasks and hardware. It starts by defining 'computational cells' (Claim 1), which are like small, reusable blueprints for parts of a neural network, composed of nodes (representing data) and edges (representing operations). For each operation, the system replaces it with a 'linear combination of candidate operations' (Claim 2), where each candidate operation has a 'mixing weight' controlled by 'computational cell hyper parameters.' The system then repeatedly adjusts these hyperparameters and weights to optimize how well the network performs (minimize 'validation loss function') while strictly adhering to 'computational resource constraints' (Claim 3, 9), such as limits on memory, processing power (float point operations), or how fast it can make predictions (inference speed). Finally, it uses these optimized settings to generate the complete neural network (Claim 1). For example, this could be used to create a highly efficient AI model for a smartwatch that needs to recognize speech without draining its small battery or exceeding its limited memory. ## What it does NOT cover - Does not cover manual design of neural network architectures by human engineers. - Does not cover architecture search methods that do not use 'computational cells' defined as directed graphs of nodes and edges (Claim 1). - Does not cover optimization methods that do not involve replacing operations with a 'linear combination of candidate operations' (Claim 2). - Does not cover optimization without specific 'computational resource constraints' (Claim 1, 9) like memory, FLOPS, or inference speed. - Does not cover methods that do not iteratively adjust 'computational cell hyper parameters and computational cell weights' during the optimization process (Claim 3). ## The clever bit The novelty lies in automatically searching for optimal neural network architectures by representing network operations as flexible 'linear combinations' of candidate options. This allows for simultaneous optimization of these combinations and the network's overall structure (computational cells) under explicit, user-defined resource limits, systematically balancing performance with real-world hardware constraints. ## Real-world examples 1. AI models for smartwatches 2. On-device speech recognition on smartphones 3. Edge AI for security cameras and drones 4. Machine learning in embedded systems for industrial control 5. Efficient AI for Internet of Things (IoT) devices ## Why it matters This technology is crucial for deploying powerful AI models on devices with limited processing power, such as smartphones, smart home devices, or embedded systems. By automatically finding efficient network designs, it reduces the need for human experts to manually fine-tune models, saving time and resources. This allows AI to be integrated into more everyday products without requiring expensive, high-end hardware, making AI more pervasive and accessible. ## Frequently asked questions ### What does How AI Automatically Builds Efficient Neural Networks for Limited Devices cover? Google's patent describes a method for automatically designing efficient neural networks by optimizing their internal structure, called 'computational cells,' to meet specific limits on resources like memory or processing speed. ### Who owns patent US 20220414425? This patent is owned by Google. ### When does this patent expire? This patent is expected to expire on August 19, 2042, when the invention enters the public domain. ### What is patent US 20220414425 cited by? This patent has been cited by 2 later patents that build on its ideas. ### What problem does this patent solve? This technology is crucial for deploying powerful AI models on devices with limited processing power, such as smartphones, smart home devices, or embedded systems. By automatically finding efficient network designs, it reduces the need for human experts to manually fine-tune models, saving time and resources. This allows AI to be integrated into more everyday products without requiring expensive, high-end hardware, making AI more pervasive and accessible. ### What does this patent NOT cover? Does not cover manual design of neural network architectures by human engineers. **Full plain-English explainer:** https://patentbrief.org/patent/us/20220414425/resource-constrained-neural-network-architecture-search **Original patent:** https://patents.google.com/patent/US20220414425 --- _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 AI Designs Its Own Brains: Hierarchical Neural Architecture Search](https://patentbrief.org/patent/us/12632737/using-hierarchical-representations-for-neural-network-architecture-searching) — This patent describes a computer method for automatically designing neural networks by representing their structures as multi-level graphs and evolving the best designs based on how well they perform. - [How AI Learns to Run Faster on Specific Computer Chips](https://patentbrief.org/patent/us/20250371349/methods-and-apparatus-for-hardware-aware-machine-learning-model-training) — 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. - [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 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. - [How AI at the Edge Learns and Adapts Its Brain](https://patentbrief.org/patent/us/20250292091/systems-and-methods-for-dynamic-neural-network-enhancement-and-adaptive-edge-com) — This patent describes how an artificial intelligence system running on a small device can automatically improve its accuracy and efficiency by changing its internal structure and learning methods in real-time.