Video analytics use cases Enterprise users are increasingly interested in implementing complex video analytics use cases that provide business value beyond typical applications. These involve multi-stage models for object detection and image classification with custom trained models that are integrated to solve business problems. Some examples include: Segment Use case Deep learning tasks Retail Cashier-less
This post is a follow-up to “Implementing a CPU-based real-time video analytics pipeline,” where we discussed a CPU-based end-to-end video analytics pipeline. As seen in that post, a CPU-based pipeline runs into severe performance bottlenecks. Here we discuss how we address and overcome these bottlenecks using FPGAs as hardware accelerators. We explain Megh’s Video Analytics
In part 1 (Megh’s platform strategy), we talked about the increasingly rapid explosion of data (from the Web, sensors, IOT devices, etc.), the need for efficient processing, and the value proposition of our real-time streaming analytics platform from the perspective of CIOs, data scientists, and developers. In this post, we discuss how the vendor-agnostic architecture
Four months into 2020, the world is facing a grave global health crisis: the outbreak of a novel coronavirus respiratory disease, COVID-19. Can new digital technology be used to help identify and mitigate the impact of COVID-19? Thankfully, the answer is yes. One technology now in the forefront of the global emergency is the use
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
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
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
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