# How a Single Electronic Component Can Learn and Process AI Data > 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. - **Patent:** US 10248907 - **Original title:** Resistive processing unit - **Owner:** International Business Machines - **Granted:** 2019 - **Status:** Active - **Times cited:** 11 - **Field:** semiconductors, ai_ml, consumer_electronics, software ## What it does This patent details a two-terminal resistive processing unit (RPU) that functions as a neuron in a neural network. Its electrical resistance represents a 'weight' for that neuron, which is a crucial value in AI learning. The RPU is designed to change its resistance in a random, or 'stochastic', and 'non-linear' way based on electrical signals applied to its two terminals (Claim 1). This change in resistance allows the RPU to 'locally perform a data storage operation' (Claim 3) for the neural network's training. Crucially, it also 'locally performs a data processing operation' (Claim 1) using this changed resistance. For example, in a system learning to recognize images, each RPU could adjust its resistance based on input signals, effectively 'remembering' a part of the image, and then use that 'memory' to help process new images, all within the same tiny component. ## What it does NOT cover - Does not cover RPUs that use more than two terminals for their operation. - Does not cover RPUs where the resistance changes in a predictable, non-stochastic manner. - Does not cover RPUs where the change in resistance is a simple linear relationship, rather than a non-linear one (Claim 1). - Does not cover systems where data storage and processing are handled by separate, distinct components, rather than locally within the RPU (Claim 3). - Does not cover RPUs that use encoding methods other than stochastic pulse sequences or magnitude modulation for input signals (Claims 6 and 7). ## The clever bit The novelty lies in designing a single, two-terminal resistive unit that can both store data (by changing its resistance) and process data, performing these functions locally and stochastically. This dual capability, especially the non-linear and stochastic resistance change, is key to efficiently implementing neural network 'weights' and 'training' directly in hardware. ## Real-world examples 1. Neuromorphic computing chips 2. AI accelerators for edge devices 3. Resistive random-access memory (RRAM) arrays used for in-memory computing 4. IBM's AI hardware research ## Why it matters This patent addresses a core challenge in artificial intelligence: how to make AI systems more energy-efficient and faster by integrating memory and processing. By allowing a single component to both store and process data, it paves the way for 'neuromorphic' hardware that mimics the brain's structure. This approach can significantly reduce the need to constantly move data between separate processing and memory units, which is a major bottleneck in traditional computer architectures. ## Frequently asked questions ### What does How a Single Electronic Component Can Learn and Process AI Data cover? 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. ### Who owns patent US 10248907? International Business Machines owns this patent, granted in 2019. ### When does this patent expire? This patent is expected to expire on October 20, 2035, when the invention enters the public domain. ### What is patent US 10248907 cited by? This patent has been cited by 11 later patents that build on its ideas. ### What problem does this patent solve? This patent addresses a core challenge in artificial intelligence: how to make AI systems more energy-efficient and faster by integrating memory and processing. By allowing a single component to both store and process data, it paves the way for 'neuromorphic' hardware that mimics the brain's structure. This approach can significantly reduce the need to constantly move data between separate processing and memory units, which is a major bottleneck in traditional computer architectures. ### What does this patent NOT cover? Does not cover RPUs that use more than two terminals for their operation. **Full plain-English explainer:** https://patentbrief.org/patent/us/10248907/resistive-processing-unit **Original patent:** https://patents.google.com/patent/US10248907 --- _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 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. - [How Hopfield Networks Use Resistors to Mimic Brain-Like Memory](https://patentbrief.org/patent/us/4660166/electronic-network-for-collective-decision-based-on-large-number-of-connections-between-signals) — A foundational patent describing an electronic circuit that uses a grid of resistors to perform computations, effectively creating an artificial neural network that can store and recall patterns. - [How to Save and Reuse Skills Learned by Artificial Intelligence Hardware](https://patentbrief.org/patent/us/10410117/method-and-a-system-for-creating-dynamic-neural-function-libraries) — A method for capturing the internal settings of a neuromorphic AI chip after it learns a task, allowing that 'skill' to be exported and loaded onto another AI chip. - [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.