Advanced Micro Devices had 34 patents in artificial intelligence during Q1 2024. The patents filed by Advanced Micro Devices Inc in Q1 2024 focus on optimizing data processing pipelines, detecting anomalies in network traffic at edge nodes using machine learning, processing data using neural networks, generating instructions for data processing arrays and microcontrollers, and creating tag bindings for assigning tags to data for different tenants using a data processing unit. These innovations aim to improve efficiency, security, and performance in data processing and network operations. GlobalData’s report on Advanced Micro Devices gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Advanced Micro Devices grant share with artificial intelligence as a theme is 41% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Memory control for data processing pipeline optimization (Patent ID: US20240103719A1)
The patent filed by Advanced Micro Devices Inc. describes a system for generating optimization instructions for data processing pipelines. This system computes resource usage information and data storage information for each stage of the pipeline to control memory operations efficiently. The optimization instructions generated by the system can discard data, control evicting, writing-back, or prefetching data to minimize latency during pipeline execution.
The method outlined in the patent involves computing resource and memory usage information for each stage of a data processing pipeline, generating data storage information, and creating optimization instructions based on this data. These optimization instructions can efficiently manage memory operations, including discarding data that is no longer needed, evicting or writing-back data to minimize latency, and prefetching data as required. The system aims to enhance the performance of data processing pipelines by optimizing memory usage and data flow between pipeline stages, particularly beneficial for machine learning pipelines or algorithms involving compute kernels or shaders.
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