Issue link: https://resources.mouser.com/i/1437726
Adam Kimmel, Mouser Electronics AI disrupts all industries, all with different data processing needs. Enter persistent memory, where greater amounts of memory enable workload consolidation to focus on fewer nodes, reducing deployments and maximizing processors previously operating below capacity because of memory constraints. 11 INTEL 2020 t he Internet of Things (IoT) has moved into the mainstream, and is poised to connect nearly 42 billion devices by 2025, according to the International Data Corporation (IDC). IoT-connected devices will generate up to 80 zettabytes of data, a 480 percent increase from 2019. The exponential growth and massive data quantity described by these projections highlight the need for a different kind of memory structure. The data must be collected, analyzed, transmitted, and ultimately stored at a scale that technology has never seen. The scale of these functions creates opportunities for innovation in data processing, and the race is on to construct the most optimal solution. Dynamic Random Access Memory (DRAM) is a popular semiconductor memory technology to optimize or improve processing time and access to stored media. The system provides data storage cells a new charge periodically to mitigate capacitor charge inefficiency. DRAM has become best-in-class for electronics memory and critical to artificial intelligence (AI) because of the continual updating of the data. The challenge has become persistent memory's cost, with the market share dominated by three principal suppliers and its complicated manufacturing process. Persistent MeMory enables WidesPread ai