{
  "patent_number": "US 10740671",
  "country": "US",
  "title": "How IBM Uses Resistive Memory Chips to Speed Up AI Training",
  "original_title": "Convolutional neural networks using resistive processing unit array",
  "summary": "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.",
  "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"
  ],
  "filed": "2017-04-06",
  "granted": "2020-08-11",
  "expires": "2037-04-06",
  "status": "active",
  "holder": "International Business Machines",
  "holder_url": "https://patentbrief.org/company/international-business-machines",
  "inventors": [
    {
      "name": "Tayfun Gokmen",
      "url": "https://patentbrief.org/inventor/tayfun-gokmen"
    }
  ],
  "times_cited": 7,
  "tags": [
    "semiconductors",
    "ai_ml",
    "consumer_electronics"
  ],
  "abstract": "Technical solutions are described for implementing a convolutional neural network (CNN) using resistive processing unit (RPU) array. An example method includes configuring an RPU array corresponding to a convolution layer in the CNN based on convolution kernels of the layer. The method further includes performing forward pass computations via the RPU array by transmitting voltage pulses corresponding to input data to the RPU array, and storing values corresponding to output currents from the RPU arrays as output maps. The method further includes performing backward pass computations via the RPU array by transmitting voltage pulses corresponding to error of the output maps, and storing the output currents from the RPU arrays as backward error maps. The method further includes performing update pass computations via the RPU array by transmitting voltage pulses corresponding to the input data of the convolution layer and the error of the output maps to the RPU array.",
  "url": "https://patentbrief.org/patent/us/10740671/convolutional-neural-networks-using-resistive-processing-unit-array",
  "markdown_url": "https://patentbrief.org/patent/us/10740671/convolutional-neural-networks-using-resistive-processing-unit-array/md",
  "google_patents_url": "https://patents.google.com/patent/US10740671",
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}