The technology industry continues to be a hotbed of innovation, with activity driven by the exponential growth of data, the need for intelligent data management solutions, and the demand for faster and more efficient storage and retrieval processes, as well as growing importance of technologies such as deep learning architectures including autoencoders and variational autoencoders that enable effective data compression and reconstruction. By applying AI techniques to data storage, neural networks provide enhanced capabilities for data management, enabling efficient storage, retrieval, and analysis of vast amounts of information. In the last three years alone, there have been over 3.6 million patents filed and granted in the technology industry, according to GlobalData’s report on Innovation in Artificial Intelligence: Neural networks for data storage. Buy the report here.
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However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
300+ innovations will shape the technology industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the technology industry using innovation intensity models built on over 2.5 million patents, there are 300+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, finite element simulation, ML-enabled blockchain networks and Generative adversarial networks (GAN), are disruptive technologies that are in the early stages of application and should be tracked closely. Demand forecasting applications, intelligent embedded systems, and deep reinforcement learning are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are, wearable physiological monitors, smart lighting, and smart climate control systems, which are now well established in the industry.
Innovation S-curve for artificial intelligence in the technology industry
Neural networks for data storage is a key innovation area in artificial intelligence
Data storage using neural networks involves employing interconnected nodes within an artificial intelligence (AI) system to effectively store and retrieve data. Neural networks consist of multiple layers of interconnected nodes that can learn from processed data and efficiently store substantial amounts of information. With their ability to recognise patterns in data, neural networks are well-suited for complex data storage tasks. Their applications encompass diverse areas, including image recognition, natural language processing, and forecasting, owing to their capacity for learning and processing complex data.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 30+ companies, spanning technology vendors, established technology companies, and up-and-coming start-ups engaged in the development and application of neural networks for data storage.
Key players in neural networks for data storage – a disruptive innovation in the technology industry
‘Application diversity’ measures the number of different applications identified for each relevant patent and broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to neural networks for data storage
Source: GlobalData Patent Analytics
Intel is a leading patent filer in neural networks for data storage. One of the company’s patents focuses on a compute apparatus designed for machine learning operations. The apparatus includes a decode unit that decodes a single instruction into a decoded instruction specifying multiple operands. It also features an arithmetic logic unit consisting of a barrel shifter, an adder, and an accumulator register. The operation involves shifting the input value by the quantized weight value using the barrel shifter, adding the shifted input value to the value stored in the accumulator register using the adder, and updating the value in the accumulator register.
Other prominent patent filers in the space include International Business Machines (IBM) and SK Square.
By geographic reach, Semiconductor Energy Laboratory leads the pack, followed by Hewlett Packard Enterprise (HPE) and TDK. In terms of application diversity, Macronix International holds the top position, followed by Qualcomm and Panasonic.
Artificial intelligence (AI) innovation in neural networks for data storage involves leveraging the capabilities of neural networks to efficiently store and retrieve large volumes of data. Neural networks are trained to encode and decode data representations, enabling effective storage and retrieval mechanisms.
To further understand how artificial intelligence is disrupting the technology industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) – Thematic Intelligence.
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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
GlobalData’s Patent Analytics tracks patent filings and grants from official offices around the world. 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.
