AI & The Future of Precision Timing: Part 4

The Impact of AI on Hardware and Frequency Control Markets: Storage Solutions

NEW YORK, NY, USA, June 11, 2024 /EINPresswire.com/ -- The market for components, equipment and devices needed for the integration and expansion of AI infrastructure and applications will see growth rates in the double digits – from 15-35% depending upon the equipment market – over the next several years.

As mentioned in our previous articles on the subject, in terms of frequency control and timing components, the growth of AI will have a direct impact on both the quartz-based and non-quartz-based markets. According to Dedalus Consulting, in their recently released report Frequency Control Components, the frequency control and timing components market will exceed $12.0 billion globally in 2024 across all markets. Each of these markets will continue to be integral in the implementation of AI.

AI's influence is profound in the frequency control and precision timing industry, reshaping hardware markets and driving impressive growth. As the demand for AI-driven solutions surges, so does the need for specialized hardware to support these innovations. In this series of articles, we have and will examine how AI is spurring growth in various hardware markets, and how this growth, in turn, fuels the expansion of frequency control components markets.

The Crucial Role of Hardware in AI

AI's rapid advancement is propelled by sophisticated hardware systems tailored to handle complex computational tasks efficiently. High-performance servers, storage solutions, networking equipment, telecom infrastructure, power supply, and management systems, as well as backup and redundancy systems form the backbone of AI infrastructure. Each of these hardware components plays a pivotal role in facilitating AI-driven applications and processes. In this article, we will look at storage solutions.

Integration of Frequency Control into Hardware Systems: Storage Solutions

Storage Solutions: Architecting Data Accessibility

Conservatively, the demand for high-capacity and high-speed storage solutions driven by AI applications is projected to grow at a CAGR of approximately 12-20% over the next five years. This growth will be fueled by the exponential increase in data generated by AI workloads and the need for efficient data storage and retrieval systems.

Produced by such industry leaders as Samsung, Intel, and Micron, some examples of key storage solutions used for AI applications include:

• Solid State Drives (SSDs) provide significantly faster read and write speeds compared to traditional Hard Disk Drives (HDDs) that rely on spinning magnetic disks. SSDs offer lower latency and higher input/output operations per second (IOPS), making them ideal for AI workloads that require rapid access to large datasets. Additionally, SSDs consume less power and produce less heat compared to HDDs, making them suitable for data center environments where energy efficiency is crucial.
• NVMe (Non-Volatile Memory Express) SSDs leverage the NVMe protocol, specifically designed for flash storage, to deliver even higher performance compared to traditional SATA-based SSDs. NVMe SSDs offer lower latency, higher throughput, and improved parallelism, making them particularly well-suited for AI workloads that require intense data processing. By directly connecting to the PCIe bus, NVMe SSDs bypass the limitations of SATA interfaces, enabling faster data transfer rates and reducing latency.
• Enterprise Storage Solutions incorporate advanced storage technologies like RAID (Redundant Array of Independent Disks) for data protection, deduplication, and compression to optimize storage efficiency. Moreover, enterprise storage arrays can be configured with a combination of SSDs and HDDs, utilizing the performance benefits of SSDs while maintaining cost-effectiveness by using higher-capacity HDDs for bulk storage.
• Cloud Storage Services offer features such as object storage, block storage, and file storage. Cloud storage solutions also integrate with AI and machine learning platforms, enabling seamless data ingestion, processing, and analysis in distributed computing environments.

Frequency Control and Timing Components in Storage Solutions

In SSDs and HDDs, for example, frequency control components such as Voltage-Controlled Crystal Oscillators (VCXOs) and Phase-Locked Loop (PLL) circuits play a pivotal role in regulating data access and retrieval speeds. Advanced frequency control technologies like Rubidium Oscillators (RBXOs) and Temperature-Compensated Crystal Oscillators (TCXOs) are becoming increasingly important in enhancing storage system performance to meet the escalating data storage demands propelled by AI.

Some examples of key components used across storage solution categories include:

Quartz-based crystal oscillators are widely used across various storage solutions, including SSDs, NVMe SSDs, and enterprise storage arrays, to generate stable clock signals essential for data transfer and processing. Using piezoelectric crystal resonators, these oscillators offer superior frequency stability and accuracy.

Silicon-based timing devices, such as MEMS (Micro-Electro-Mechanical Systems) oscillators, play a critical role in AI storage solutions by providing accurate clock signals for data processing, synchronization, and timing control. Their high reliability, low jitter, compact size, and power efficiency make them key components in modern AI storage architectures, enabling high-performance computing and efficient data management.

Other components include Phase-Locked Loops (PLLs), which serve to generate stable clock signals that synchronize the rotation of disk platters and the movement of read/write heads; clock buffers and drivers, which amplify and shape clock signals, minimize signal distortion, and reduce signal propagation delays, enhancing the overall reliability and performance of storage systems; and real-time clocks (RTCs), which provide accurate timekeeping functions, maintaining the current date and time even in the absence of external power and are used for timestamping data, logging system events, and scheduling maintenance tasks, facilitating efficient data management and system operation.

Looking Ahead

AI applications, including machine learning, deep learning, and neural networks, require massive amounts of data for training and inference processes. As AI algorithms become more complex and sophisticated, the need for storage solutions with high capacity, fast data transfer rates, and low latency becomes increasingly critical.

The growth of AI is driving the adoption of larger and more advanced storage solutions across various industries, including healthcare, finance, automotive, and retail. For example, in healthcare, AI-powered diagnostic tools require vast amounts of medical imaging data, patient records, and genomic information, necessitating high-capacity and high-performance storage solutions.

AI is also driving innovations in storage architecture and technologies. For example, AI workloads benefit from optimized storage systems that can handle parallel data processing and support specialized data formats used in deep learning frameworks.

As the demand for storage solutions grows with the proliferation of AI applications, the market for frequency control components also experiences a corresponding surge. These components are essential for ensuring the precise timing and synchronization required for efficient data transfer and processing in storage devices used for AI workloads.

More Information & How to Order
For more information about this report, please:
• navigate to the report page: Frequency Control & Timing Components;
• learn more about our Ulysses Data Subscription Service (USS), which covers the market through 2040;
• send us a Research Enquiry;
• email us at info@dedalusconsulting.com; or
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About Dedalus Consulting
Dedalus Consulting is a privately owned and independently operated market research publisher and consultancy.

Our research focuses on both emerging and mature markets in high-technology sectors, including tooling and machining, advanced materials, frequency control and timing, surge and circuit protection, energy and renewables, life sciences, and next generation computing. Research is continually updated through a methodology that is based on primary interviews with market participants, including manufacturers, end-users, research institutions, distribution channel representatives and service providers.

Our clients range from Fortune 500 companies to private equity and investment banking institutions to academic research organizations engaged in the research, development and manufacturing of advanced technology products and services.

Jennifer Larkin
Dedalus Consulting
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