Tag: Blog post

Implementing a CPU-based real-time video analytics pipeline

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

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Megh’s platform strategy

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

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Megh’s flexible, high-performance deep learning engine

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

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Using AI/Deep Learning to prevent retail inventory loss

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

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Megh demos Spark-based real-time streaming video analytics using FPGAs

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

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Implementing a real-time, deep learning pipeline with Spark Streaming

With the current information age defining the third wave, we are facing an explosion of real-time data, which is in turn increasing demand for real-time analytics. A real-time analytics solution pipeline typically utilizes a streaming library and an analytics platform. Apache Spark is an open-source, distributed computing platform designed to run analytics payloads on a

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What is the third wave?

Futurist Alvin Toffler introduced the idea of the third wave in his 1980 book of the same name to interpret cultural shifts based on economic drivers: The first wave was the Agricultural Era, which lasted for a millennia. This was disrupted by the Industrial Era, which started in Western Europe in the seventeenth century. The Third

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