With growth in available data and computation power, use of video analytics solutions has been growing visibly. Most real-time video analytics use-cases, however, require response times in milliseconds—a level of performance that both CPUs and GPUs cannot always meet when it comes to inference. Here we discuss implementation of a real-time video analytics pipeline on
Author: Megh Computing, Inc.
We are pleased to announce that Megh Computing signed a memorandum of understanding with the Indian Institute of Technology Goa (IIT-Goa) on August 21, 2019, for joint research in hardware-accelerated, AI-based analytics. Megh’s mission is to deliver an efficient, scalable platform for real-time analytics using FPGAs for deployment in the public domain and private cloud.
The demand for real-time stream processing is increasing rapidly with the explosion of data from the Web, sensors, IoT and mobile devices, and other sources. Enterprises want to process this data as it moves to create business value in areas such as: Finance (e.g., fraud detection, risk management) Sales and marketing (e.g., customer preferences) Operations
As deep learning (DL) becomes more pervasive, the need for efficient and fast computation is increasing. Traditional central processing units (CPUs) and graphical processing units (GPUs) are typically used for acceleration, despite the limiting nature of their fixed architectures. Field programmable gate arrays (FPGAs) have been highlighted for their flexibility, but until recently have fallen
Megh Computing has been demonstrating its Video Analytics Solution on the Intel platform. The Video Analytics Solution (VAS) is based on Megh’s Real Time Streaming Analytics platform and targeted for various use cases, including fraud prevention in the retail supply chain, inventory tracking in manufacturing, and video surveillance for security. Demos took place at: Intel
Retail inventory loss (or “shrinkage”) is a serious problem, totaling about $100 billion annually—almost 1.8% of sales—worldwide. The issue is even more acute for those moving high-dollar goods, such as fashion and accessories. As traditional retailers grapple with ongoing market concentration, loss of market share to online sellers, and other pressures, there’s good news: Advanced
Megh computing demonstrated “Spark-based real-time streaming video analytics using FPGAs” at the Intel booth during the Spark+AI Summit in April 2019. Key features of the demo included: Streaming analytics at scale with Spark Streaming and Analytics Zoo Real-time performance with Intel FPGA analytics pipelines acceleration Enabled by Megh Computing for various use cases with low
Megh Computing last week won the Technology Association of Oregon (TAO) 2019 Technology Company of the Year award in their Pre-Revenue category. Winners were selected from a wide spectrum of industries and recognized for their excellence and achievement within the region’s vibrant technology community. Other Technology Company of the Year winners were Stackery (in the
Provides an overview on overcoming the limits of data center infrastructure and the challenges of FPGA deployment Portland, OR (April 18, 2019)—Megh Computing, Inc. today released a white paper for enterprise strategy and IT personnel titled “Using FPGAs for advanced real-time analytics of streaming data,” providing an overview of problems of and solutions to extracting