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Micron B47R 3D CTF CuA NAND Die, World’s First 176L (195T)


Micron’s 176L 3D NAND is the world’s first 176L 3D NAND Flash memory. TechInsights just found the 512Gb 176L die (B47R die markings) and quickly viewed its process, structure, and die design. Micron 176L 3D NAND is one of the most groundbreaking technologies to date, and it is especially for the storage application such as data center, 5G, AI, cloud, intelligent edge, and mobile devices. Mi... » read more

Next-Gen SerDes Roadmap


An explosion in data is causing a series of successive bottlenecks in the data center. Priyank Shukla, product marketing manager for high-speed SerDes IP at Synopsys, digs into the performance roadmap for moving data within server racks and between different racks, where the bottlenecks are today, and how they will be addressed in the future. Related SerDes Knowledge Center Top stories... » read more

Computing Where Data Resides


Computational storage is starting to gain traction as system architects come to grips with the rising performance, energy and latency impacts of moving large amounts of data between processors and hierarchical memory and storage. According to IDC, the global datasphere will grow from 45 zettabytes in 2019 to 175 by 2025. But that data is essentially useless unless it is analyzed or some amou... » read more

Nine Effective Features Of NVMe Verification IP For PCIe-Based SSD Storage


Non-Volatile Memory Express (NVMe) is a new software interface optimized for PCIe Solid State Drives (SSD). This paper provides an overview of the NVMe specification and examines some of its key features. We will discuss its pros and cons, compare it to other conventional technologies, and point out key areas to focus on during its verification. You will learn how NVMe Questa Verification IP... » read more

Evaluating NVMe SSD Multi-Gigabit Performance


The multi-channel parallelism and low-latency access of NAND flash technology have made Non-Volatile Memory express (NVMe) based SSDs very popular within the main segments of the data storage market, including not only the consumer electronics sector but also data center processing and acceleration services, where the key role is played by specialized FPGA-based hardware for application-specifi... » read more

Transient Thermal Analysis For M.2 SSD Thermal Throttling: Detailed CFD Model vs Network-Based Model


Solid State Drive (SSD) technology continues to advance toward smaller footprints with higher bandwidth and adoption of new I/O interfaces in the PC market segment. Power performance requirements are tightening in the design process to address specific requirement along with the development of SSD technology. To meet this aggressive requirement of performance, one major issue is thermal throttl... » read more

Utilizing Computational Memory


For systems to become faster and consume less power, they must stop wasting the power required to move data around and start adding processing near memory. This approach has been proven, and products are entering the marketplace designed to fill a number of roles. Processing near memory, also known as computational memory, has been hiding in the shadows for more than a decade. Ever since the... » read more

Solving The Memory Bottleneck


Chipmakers are scrambling to solve the bottleneck between processor and memory, and they are turning out new designs based on different architectures at a rate no one would have anticipated even several months ago. At issue is how to boost performance in systems, particularly those at the edge, where huge amounts of data need to be processed locally or regionally. The traditional approach ha... » read more

Trading Off Power And Performance Earlier In Designs


Optimizing performance, power and reliability in consumer electronics is an engineering feat that involves a series of tradeoffs based on gathering as much data about the use cases in which a design will operate. Approaches vary widely by market, by domain expertise, and by the established methodologies and perspective of the design teams. As a result, one team may opt for a leading-edge des... » read more

Power/Performance Bits: April 16


Faster CNN training Researchers at North Carolina State University developed a technique that reduces training time for deep learning networks by more than 60% without sacrificing accuracy. Convolutional neural networks (CNN) divide images into blocks, which are then run through a series of computational filters. In training, this needs to be repeated for the thousands to millions of images... » read more

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