Currently, there are more devices connected to the Internet than there are people in the world. The Internet of Things (IoT) evolves exponentially and as a result, increases the physical representations of data accessible via Internet systems.
In view of the limitations of application memory size and their random-access nature, processors considerably lack energy-efficiency. Therefore, arises the need for dedicated hardware for IoT-generated data storage, effectively replacing software to accelerate the overall system․
Grovf offers GCache - an FPGA-based key-value store designed to efficiently store and retrieve cached data.
Fast analysis of semi-structured or unstructured data is crucial for business decision making.
The amount of information, having no standard format (images, emails, text, XML, videos, etc.) continues to grow due to the high reliance on digital content in computer systems and makes searching/analysis more complex.
According to IDC predictions, 85% of the generated data will be unstructured by 2025 and much of this will be in a textual form. As the largest data source, unstructured data becomes a large ground for analytics and deploying AI applications in the company.
To do analytical processing against unstructured textual data, companies usually confront several obstacles and need to use specific approaches to handle them, such as Regular expression algorithms.
The enormous growth in data constantly challenges computing technologies and traditional approaches we used to demonstrate. Memories barely keep pace with current tremendous growth of data and processors advancements, turning into a core bottleneck for computational systems.
Though computer technology has made incredible progress throughout the time, the dropping efficiency of modern computing points towards much more constructive changes in architecture design, known as memory-centric architecture.
Aiming to get the most out of memory-centric architecture, Grovf develops MonetX technology that expands the memory of the existing servers to several TBs per server while accelerating the near memory computing workloads.