Category: Real-time Analytics

REST and WebSocket APIs in Nimble

The Nimble application framework Nimble is a fast and lightweight service-based framework for implementing video analytics pipelines targeted for CPU, GPU, FPGA, and SOC platforms. As illustrated below, Nimble sits on top of the Arka Runtime, which manages deep learning inferencing on the targeted platforms and enables seamless integration of The Megh Platform into existing

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Megh VAS SDK

Megh’s fully customizable, cross-platform Video Analytics Solution (VAS) is available as the VAS SDK toolkit and VAS Suite of products. VAS SDK is targeted for enterprises, system integrators (SI), OEMs, and developers, enabling full control to optimize video analytics pipelines and integrate highly customizable AI into applications. VAS SDK is one member of Megh’s family

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Open Analytics: A new approach to intelligent video analytics

The demand for edge analytics is increasing rapidly with the explosion of streaming data from sensors, cameras, and other sources. Of these, video remains the dominant data source with over a billion cameras deployed globally. Enterprises want to extract intelligence from these data streams to create business value. As such, over the past decade, we’ve

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Nimble application framework with cross-platform support

With the release of Megh VAS 100 comes a new addition to the Megh Computing solution stack: the Nimble application framework. Nimble is a fast and lightweight service-based framework for implementing CPU, GPU, and FPGA video analytics pipelines. As illustrated below, Nimble sits on top of Arka, Sira, and Deep Learning Engine (DLE), enabling seamless

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Megh’s Deep Learning Engine usages

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

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Implementing a CPU+FPGA-based real-time video analytics pipeline

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

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Megh’s platform strategy — Part 2

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

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Video analytics use cases to manage COVID-19 response

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

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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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