A Growing Requirement for Increased Memory Capacities
Leading-edge technologies, such as big data analytics and deep learning (a specific subset of artificial intelligence), are creating an increasing demand for memory capacity and performance inside the data center. For example, deep learning techniques that leverage neural networks are being used in autonomous driving applications for voice or facial recognition and are required to analyze and compute large data sets at incredible speeds. Due to this, the memory bandwidth and capacity in the system directly define the run time of a learning algorithm and are often the limiting factor for the system’s performance.
In addition to these emerging technologies, there are also many current applications that could benefit from a cost-effective way to significantly increase memory capacity. Examples include in-memory databases (IMDB) for faster decision making, media streaming services that could load full movies into memory, large graphic rendering projects (animated movie or game creation) that constantly require terabytes of data to be loaded into memory at a time, and more. Credit card fraud detection is a great example use case that could benefit from large-capacity IMBDs. The bank behind a credit card is required to approve or reject a transaction from any user at any given moment in time, which requires them to quickly access large amounts of information every transaction. Having decision making information directly available in-memory (versus disk storage) will allow the bank to make quicker, more accurate decisions and could help prevent fraud from occurring.
As seen in the examples above, technology continues advancing and data generation from edge devices is exponentially increasing. Therefore, the requirement for instantaneous calculations is higher than ever, and projects like our hybrid memory research are solving key limitations that will continue advancing hardware and reduce bottlenecks in the data center.