MP3000 pSLC | PCIe NVMe | M.2 2280 SSD

SafeDATA technology combines unique power loss detection and hold-up circuitries with an advanced controller firmware algorithm to flush in-flight data from volatile cache to NAND Flash memory in order to safeguard data against corruption and/or loss during the sudden power loss.

A Single Event Upset (SEU) is an inadvertent change in bit status occurring in a digital system when a high energy neutron or alpha particle randomly strikes causing a memory bit to flip states. These high-energy particles can originate from terrestrial or extra-terrestrial sources such as cosmic rays.

SMART’s Advanced Error Detection & Correction technology reinforces the ECC (Error Code Correction) engine and utilizes RAID (Redundant Array of Independent Disks) mechanism. Data is reconstructed by the prior stored parity in other pages. The recovered data will be stored in a new block, and the prior stored block will be refreshed.

End-to-End Protection ensures data is correctly transferred at every data transfer point within an SSD by utilizing Error Correction Code (ECC) and additional protection mechanisms, such as Cyclic Redundancy Check (CRC), to detect and rectify errors.

S.M.A.R.T. is a self-monitoring system that prevents risks from unscheduled downtime by monitoring and displaying critical drive information. The indicators includes the drive’s health, status, usage info and potential disk problems.

Advanced Encryption Standard (AES) is a hardware-based encryption method for converting data from an unencrypted into an encrypted format. 256 bits encryption key length makes it virtually impossible to decrypt the data without the original key.

Trusted Computing Group (TCG) Storage Work Group created the Opal Security Subsystem Class (SSC) as one class of security management protocol for storage devices. It is the most recognized standard for self-encrypting drives (SEDs). SMART offers TCG Opal 2.0 compliant self-encrypting SSDs incorporating AES encryption for rock-solid data protection.

Along with increasing amount of IT devices and systems installed in modern enterprises, more and more data is generated and transported everyday between physical equipment and virtual spaces, like the cloud. In order to efficiently and effectively store and manage these quantities of data, it’s becoming necessary to build a data infrastructure that can accommodate in-house data centers or that can be outsourced to cloud service providers. Whether the data infrastructure is internal or external, the main purpose is to gather process and store the data under stable and secure conditions to ensure uninterrupted operation for the enterprises.

Whether it’s a mid-size health insurance company or a large-scale e-commerce service provider, storage servers play an important role in terms of data management for any business operation. Along with the increasing amounts of data generated and transported through network connections, it’s crucial to efficiently process and store the data, while enabling data access, yet maintaining security. Whatever the use or data applications, storage servers can are used where a large amount of data is transmitted, gathered and processed for analysis and business operations.

With the rapid rise of IoT and IIoT, the demand for connectivity has transformed networking. Today’s networks are scaling at an exponential rate, processing huge amounts of data that are stored for analysis. Reliable, proven memory is vital to ensure the hyper-fast transmission of all that data, as well as storage of the data exchanged between edge devices and network hubs. Whether the scale of network is between two locations or built for thousands of connected devices in different places around the world, there will be huge amounts of data constantly being processed whenever the network is running. That is why the right memory solutions are so important – to ensure data will be processed quickly and stored securely regardless of the demand placed on the network.

From small industrial components deployed in automation systems to large equipment used for oil drilling, project operations are asked to provide precise instructions, occasionally in harsh environments. Therefore, it is critical to maintain the accuracy of data output in addition to maintaining continuous, stable operating performance.

Accelerated AI and ML workloads often require high bandwidth memory to keep up with the massive amounts of data being processed. There's a need for memory architectures that can deliver higher bandwidth to match the computational capabilities of modern accelerators. This could involve innovations in DRAM design, such as wider memory buses, faster memory interfaces, or the integration of high-bandwidth memory (HBM) technologies.

High Performance Computing (HPC) represents a leading solution being used to model and understand complex issues such as weather, agriculture, and space. HPC applications require the ability to process data and perform complex calculations at high speeds in limited timeframes. For applications such as AI machine/deep learning, data analysis, or medical research, HPC can process massive amount of data in real time. As HPC increases, so will the demands for high performing and reliable memory to deliver on expectations.