# How IBM Uses Resistive Memory Chips to Speed Up AI Training > A method for running AI neural networks directly on specialized hardware chips that store data as electrical resistance, making them faster and more energy-efficient than standard processors. - **Patent:** US 10740671 - **Original title:** Convolutional neural networks using resistive processing unit array - **Owner:** International Business Machines - **Granted:** 2020 - **Status:** Active - **Times cited:** 7 - **Field:** semiconductors, ai_ml, consumer_electronics ## What it does This patent describes a way to perform the heavy math required for artificial intelligence (specifically convolutional neural networks) directly on a grid of resistive memory devices. Instead of moving data back and forth between a processor and memory, the system uses the physical properties of the memory cells themselves to perform calculations. By applying voltage pulses to these cells, the system can calculate forward passes, backward passes, and weight updates simultaneously across the entire array. This allows the hardware to handle complex image recognition tasks by using the flow of electricity through the grid to represent mathematical operations. ## What it does NOT cover - Does not cover standard digital processors like CPUs or GPUs that rely on traditional binary logic gates - Does not cover software-only implementations of neural networks that run on conventional computer architectures - Does not cover non-resistive memory technologies like traditional DRAM or SRAM for performing these specific analog computations ## The clever bit It treats the physical resistance of a memory cell as a mathematical weight, turning the entire memory array into a giant, parallel calculator that computes matrix multiplications using Ohm's Law. ## Real-world examples 1. Experimental IBM RPU (Resistive Processing Unit) chips 2. In-memory computing hardware prototypes 3. Analog AI accelerators ## Why it matters Training large AI models is incredibly energy-intensive and slow on current hardware. By moving the computation into the memory itself, IBM's approach aims to drastically reduce the energy cost and time required to train AI. This is a significant step toward 'in-memory computing,' which is a major focus for companies trying to build more sustainable and powerful AI hardware. ## Frequently asked questions ### What does How IBM Uses Resistive Memory Chips to Speed Up AI Training cover? A method for running AI neural networks directly on specialized hardware chips that store data as electrical resistance, making them faster and more energy-efficient than standard processors. ### Who owns patent US 10740671? International Business Machines owns this patent, granted in 2020. ### When does this patent expire? This patent is expected to expire on April 6, 2037, when the invention enters the public domain. ### What is patent US 10740671 cited by? This patent has been cited by 7 later patents that build on its ideas. ### What problem does this patent solve? Training large AI models is incredibly energy-intensive and slow on current hardware. By moving the computation into the memory itself, IBM's approach aims to drastically reduce the energy cost and time required to train AI. This is a significant step toward 'in-memory computing,' which is a major focus for companies trying to build more sustainable and powerful AI hardware. ### What does this patent NOT cover? Does not cover standard digital processors like CPUs or GPUs that rely on traditional binary logic gates **Full plain-English explainer:** https://patentbrief.org/patent/us/10740671/convolutional-neural-networks-using-resistive-processing-unit-array **Original patent:** https://patents.google.com/patent/US10740671 --- _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 a Single Electronic Component Can Learn and Process AI Data](https://patentbrief.org/patent/us/10248907/resistive-processing-unit) — This patent describes a tiny electronic component called a resistive processing unit (RPU) that acts like a brain cell in an artificial intelligence network, storing and processing information directly within its changing electrical resistance. - [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. - [How a Chip Uses Memory to Speed Up AI Calculations](https://patentbrief.org/patent/us/11741188/hardware-accelerated-discretized-neural-network) — This patent describes a specialized computer chip that uses non-volatile memory and analog signals to quickly perform calculations for artificial intelligence, especially for neural networks that need to remember past information. - [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. - [How Nanoparticles Form Adaptive Neural Network Connections](https://patentbrief.org/patent/us/8156057/adaptive-neural-network-utilizing-nanotechnology-based-components) — This patent describes how to build and strengthen a physical neural network using tiny nanoparticles suspended in a liquid, where electric fields make the connections learn and adapt.