FPGA Acceleration for Big Data & AI Computing

Turning challenges into opportunities

Grovf offers products that accelerate big-data analytics and enable organizations to search and analyze the most critical data at speeds approaching real-time.

The Grovf platform takes advantage of the versatile FPGA HWs to accelerate the processing of computationally intensive big-data. We do this by accelerating middleware, so applications running on top of middleware get an automatic boost without any changes on the user application.

Read more about the product on Xilinx® Alveo™ Data Center accelerator cards.

10x

Faster Insights

10x

Real-Time Infrastructure

2x

Save on TCO

Grovf's Platform for Big Data Computing

Grovf's Platform for  Big Data Computing
Security Log Analytics

Security Log Analytics

Databases provide a wealth of functionality to a wide range of applications. Yet, there are tasks for which they are less than optimal, for instance when processing becomes more complex or the data is less structured. As data is exploding exponentially only CPU based Cybersecurity brings dual challenge of low-latency detection and remediation of advanced threats, and batch analysis of log data from servers, firewalls, applications and security systems. Considering how fast new threats and attacks emerge, Big Data performance and the use of new types of software and hardware accelerators is becoming more


Financial Fraud/Risk Analytics

Deep packet inspection (DPI) is an advanced method of examining and managing network traffic. It is a form of packet filtering that locates, identifies, classifies, reroutes or blocks packets with specific data or code payloads that conventional packet filtering, which Cybersecurity brings dual challenge of low-latency detection and remediation of advanced threats, and batch analysis of log data from servers, firewalls, applications and security systems. Considering how fast new threats and attacks emerge, Big Data performance and the use of new types of software and hardware accelerators is becoming more

Financial Fraud/Risk Analytics

User Sentiment Analytics

User Sentiment Analytics

mplementing effective cybersecurity measures is particularly challenging today because there are more devices than people, and attackers are becoming more innovative. Cybersecurity brings dual challenge of low-latency detection and remediation of advanced threats, and batch analysis of log data from servers, firewalls, applications and security systems. Considering how fast new threats and attacks emerge, Big Data performance and the use of new types of software and hardware accelerators is becoming more