Hitachi has developed an arithmetic operation device for neural networks, optimizing convolution operations by reducing and adding bits in target data and filter weights. This innovation enhances efficiency in convolution layers. GlobalData’s report on Hitachi gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Hitachi, M2M communication interfaces was a key innovation area identified from patents. Hitachi's grant share as of January 2024 was 23%. Grant share is based on the ratio of number of grants to total number of patents.

Arithmetic operation device for convolution arithmetic operation in neural networks

Source: United States Patent and Trademark Office (USPTO). Credit: Hitachi Ltd

A recently granted patent (Publication Number: US11886874B2) discloses an arithmetic operation device designed to enhance the efficiency of convolution arithmetic operations within neural networks. The device includes a bit reduction unit that reduces the bit strings of target data and filter weights, along with a bit addition unit that adds a calculated bit string to the convolution arithmetic operation result. Additionally, a determiner is incorporated to monitor overflow occurrences in the convolution arithmetic unit, ensuring optimal operation across multiple convolution layers.

Furthermore, the patent details a storage unit that stores essential information for each convolution layer, aiding in the determination of bit numbers and sizes for efficient arithmetic operations. The device also features a reduced bit number calculator that calculates the necessary bit numbers based on filter size, target data bit numbers, weight bit numbers, and adder bit numbers. By utilizing these innovative components, the arithmetic operation device aims to streamline convolution arithmetic operations within neural networks, ultimately improving computational efficiency and performance.

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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.