Mitsui. has filed a patent for a neural network generation device that creates a neural network execution model based on hardware and network information. The device includes units for model generation and software creation for running neural network hardware. GlobalData’s report on Mitsui gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Mitsui, Hydrogen storage alloys was a key innovation area identified from patents. Mitsui's grant share as of January 2024 was 39%. Grant share is based on the ratio of number of grants to total number of patents.
Neural network generation device for hardware and software integration
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The patent application (Publication Number: US20240037412A1) describes a neural network generation device that generates a neural network execution model for performing neural network operations. The device includes an execution model generation unit that generates the neural network execution model based on hardware information and network information, and a software generation unit that generates software for running neural network hardware. The software is designed to partition the neural network operations and input data into partial tensors, optimizing memory usage and data transfer for consecutive convolution operations.
Furthermore, the patent application details a neural network control method and a non-transitory computer-readable recording medium storing a software generation program. The method involves partitioning the neural network operations and input data, while the software generation program generates software for controlling neural network hardware by allocating partitioned operations to the hardware. Additionally, the software includes learned parameters related to the neural network execution model, enhancing the efficiency and performance of the neural network generation device. The device also features a storage unit for storing learned parameters and a hardware generation unit for generating a hardware model for installing the neural network execution model in the hardware. Overall, the invention aims to improve the execution and performance of neural network operations by optimizing data partitioning, memory usage, and software generation for neural network hardware.
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