STMicroelectronics had 30 patents in artificial intelligence during Q4 2023. STMicroelectronics NV filed patents in Q4 2023 for a convolutional accelerator with flexible data storage modes, a method to transform trained artificial neural networks, in-memory computation circuits with efficient processing capabilities, and a method for fully homomorphic encryption using bootstrapping keys. GlobalData’s report on STMicroelectronics gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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STMicroelectronics grant share with artificial intelligence as a theme is 60% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Acceleration of 1x1 convolutions in convolutional neural networks (Patent ID: US20230418559A1)
The patent filed by STMicroelectronics NV describes a convolutional accelerator that includes a feature line buffer, a kernel buffer, a Multiply-Accumulate (MAC) cluster, and mode control circuitry. The accelerator operates in two modes: in the first mode, feature data is stored in the feature line buffer and kernel data in the kernel buffer, while in the second mode, the storage locations are swapped. This design allows for efficient processing of 1×N kernels, where N is an integer greater than or equal to 1. The system can handle different configurations of data storage and transfer based on the mode of operation selected.
The convolutional accelerator system described in the patent includes specific configurations for storing feature data and kernel data in the buffers, transferring data to the MAC cluster, and generating output values. The system utilizes a combination of single-port and dual-port memory buffers to efficiently process convolutional operations. Additionally, the mode control circuitry determines the mode of operation based on a configuration parameter stored in a configuration register. This patent outlines a method, system, and computer-readable medium for convolving streamed kernel data with streamed feature data using the described convolutional accelerator with multiple modes of operation, providing flexibility and efficiency in processing convolutional operations.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
