COMPUTER HARDWARE MACHINE LEARNING

Brand Owner (click to sort) Address Description
ELASTIC PROCESSING UNIT SHANGHAI ILUVATAR COREX SEMICONDUCTOR CO., LTD. NO. 1628, SUZHAO ROAD, MINHANG DISTRICT SHANGHAI China computer hardware for machine learning, deep learning, computer vision, robotics, and high-performance computing capabilities from edge to cloud devices and systems;PROCESSING UNIT;
OPTRA LEXMARK INTERNATIONAL, INC. IP Legal Dept / Bldg 004-1 740 West New Circle Road Lexington KY 40550 Computer hardware for machine learning and artificial intelligence, namely, edge computing hardware for executing artificial intelligence algorithms, programs, and functions; Downloadable computer software using artificial intelligence for machine learning; Recorded computer software using artificial intelligence for machine learning;Computer software development in the field of machine learning and artificial intelligence; Platform as a service (PAAS) featuring computer software platforms using artificial intelligence for machine learning; Software as a service (SAAS) services featuring software for remote management of network connected devices; Software as a service (SAAS) services featuring software using artificial intelligence for machine learning;
 

Where the owner name is not linked, that owner no longer owns the brand

    
Technical Examples
  1. A system and method for processing machine learning techniques (such as neural networks) and other non-graphics applications using a graphics processing unit (GPU) to accelerate and optimize the processing. The system and method transfers an architecture that can be used for a wide variety of machine learning techniques from the CPU to the GPU. The transfer of processing to the GPU is accomplished using several novel techniques that overcome the limitations and work well within the framework of the GPU architecture. With these limitations overcome, machine learning techniques are particularly well suited for processing on the GPU because the GPU is typically much more powerful than the typical CPU. Moreover, similar to graphics processing, processing of machine learning techniques involves problems with solving non-trivial solutions and large amounts of data.