3D NAND And NOR Drive Faster Interfaces, New Form Factors And Applications

Forbes - Tech

Solid state drive and drive enabling technology continues to roll out. I will try to capture some of the latest developments that I have seen over the last month and a half. These range from an update on the PCIe generation 5.0 interface, new SSD controllers from Marvell for 96-layer NAND flash, Kingston client SSDs and a new NOR product family geared for automotive and industrial applications from Cypress. PCIe is the computer interface that NVMe is based upon. PCIe 4.0 with raw performance of 16 giga-transitions per second (GT/s) was introduced in 2017.


IBM Unlocks New Storage Secrets

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For the first time, scientists at IBM Research have demonstrated reliably storing 3 bits of data per cell using a relatively new memory technology known as phase-change memory (PCM). The current memory landscape spans from venerable DRAM to hard disk drives to ubiquitous flash. But in the last several years PCM has attracted the industry's attention as a potential universal memory technology based on its combination of read/write speed, endurance, non-volatility and density. For example, PCM doesn't lose data when powered off, unlike DRAM, and the technology can endure at least 10 million write cycles, compared to an average flash USB stick, which tops out at 3,000 write cycles. This research breakthrough provides fast and easy storage to capture the exponential growth of data from mobile devices and the Internet of Things.


Take your machine-learning workloads to the edge? Yes, says Intel

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Sponsored Artificial intelligence and machine learning hold out the promise of enabling businesses to work smarter and faster, by improving and streamlining operations or offering firms the chance to gain a competitive advantage over their rivals. But where is best to host such applications – in the cloud, or locally, at the edge? Despite all the hype, it is early days for the technologies that we loosely label "AI", and many organisations lack the expertise and resources to really take advantage of it. Machine learning and deep learning often require teams of experts, for example, as well as access to large data sets for training, and specialised infrastructure with a considerable amount of processing power. This is because cloud service providers have a wealth of development tools and other resources readily available such as pre-trained deep neural networks for voice, text, image, and translation processing, according to Moor Insights & Strategy Senior Analyst Karl Freund.


Storage strategies for machine learning and AI workloads

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Businesses are increasingly using data assets to accelerate their competitiveness and drive greater revenue. Part of this strategy is to use machine learning and AI tools and technologies. But AI workloads have significantly different data storage and computing needs than generic workloads. AI and machine learning workloads require huge amounts of data both to build and train the models and to keep them running. When it comes to storage for these workloads, high-performance and long-term data storage are the most important concerns.


Optimizing an artificial intelligence architecture: The race is on

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AI applications often benefit from fundamentally different architectures than those used by traditional enterprise apps. And vendors are turning somersaults to provide these new components. This complimentary document comprehensively details the elements of a strategic IT plan that are common across the board – from identifying technology gaps and risks to allocating IT resources and capabilities. You forgot to provide an Email Address. This email address doesn't appear to be valid.