GRegeX is an implementation of PCRE compatible regular expression algorithm on FPGA chip achieving 12.8 GB/s throughput with a single IP core
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Regular expressions are specially encoded text strings used as patterns for matching sets of strings. GRegeX is an implementation of standard regular expression algorithm on FPGA chip achieving 12.8 GB/s throughput with a single IP core. Wide range of supported regular expression functions allows developers configure desired rules which can be handled in a chip without reducing the throughput. The target applications include: Smart firewalls, Security and Log Text analysis.

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

12.8 GB/s

Throughput - Single Core



Host Drivers

Easy Integration

Security Log Analytics

Security Log Analytics

Implementing 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 critical.

Technology is essential to giving organizations and individuals the computer security tools needed to protect themselves from cyber attacks. Three main entities must be protected: endpoint devices like computers, smart devices, and routers; networks; and the cloud.

Grovf's text processing FPGA cores and Open source software SDK provide a effortless way to use powerful FPGA devices for vast amount of security log analysis.

FPGA's powered with Grovf's Regex, Exact Search and Similiarity Search functions provide to organizations to analize hundreds of megabytes of data in real-time and detect security alerts. 

User Sentiment Analytics

The promise of artificial intelligence (AI) is to make work and life more productive. But to do so, AI needs to better understand humans, which are the most complex organisms on Earth. A significant element of AI’s limitations, to date, is understanding humans and, more specifically, human emotion. In the past few years, however, accelerated access to data (primarily social media feeds and digital video), cheaper compute power, and evolving deep learning combined with natural language processing (NLP) and computer vision are enabling technologists to watch and listen to humans with the intention of analyzing their sentiments and emotions. A better understanding of emotion will help AI technology create more empathetic customer and healthcare experiences, drive our cars, enhance teaching methods, and figure out ways to build better products that meet our needs.

Emotion and sentiment analysis is complex because emotion is complex and not very well understood. Emotion can be deceptive and expressed in multiple ways: in our speech intonation, the text of the words we say or write, our facial expressions, body posture, and gestures. These factors create variables in emotion analysis confidence scoring, which must be overcome for most sentiment and emotion analysis use cases to come into full bloom. Despite these challenges, the market for sentiment and emotion analysis has begun to expand. Tractica has identified seven use cases where significant direct software revenue will be generated through 2025: customer service, product/market research, customer experience, healthcare, automotive, education, and gaming.

This Tractica report examines the market and technology issues surrounding sentiment and emotion analysis and provides 9-year forecasts for software, hardware, and revenue supporting these applications. The report covers the ways in which sentiment and emotion analysis will be used across multiple channels in seven key use cases: customer service, product/market research, customer experience, healthcare, education, automotive, and gaming. It presents profiles for key industry players throughout the ecosystem. The study also presents global market sizing and forecasts for sentiment and emotion analysis, segmented by region, covering the period from 2016 through 2025.

Key Questions Addressed:

  • What is the current state of the sentiment and emotion analysis market and how will it develop over the next decade?
  • What are the key use cases that will drive greater sentiment and emotion analysis adoption?
  • What are the key drivers of market growth, and the key challenges faced by the industry, in each world region?
  • Who are the key players in the market, what is their competitive positioning, and which ones are poised for greatest success in the years ahead?
  • What is the size of the sentiment and emotion analysis market opportunity?

Who Needs This Report?

  • Artificial intelligence technology companies
  • Semiconductor and component companies
  • Customer-focused enterprises and solution providers
  • Healthcare providers and technology vendors
  • Automotive technology companies
  • Market research, advertising, brand strategy, and marketing companies
  • Government agencies
  • Investor community

User Sentiment Analytics