Tag: Blog post

Megh VAS Portal: Manage multiple sites through a single pane of glass

Megh’s VAS Portal provides an easy-to-use dashboard that can be accessed via any browser to manage multiple VAS Suite instances. Supported by a powerful microservices cluster on the backend, VAS Suite supports video analytics use cases for public safety, worker safety, and inventory management for deployment in smart buildings, smart warehouses, smart cities, and other

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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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From platform to product?

Over the past 20 years, we have seen the growth of “platform economies of scale,” which have led to a transformative business environment and changes in global economic wealth. Companies have been typically competing on products. Yet platforms generate move value. Platforms can create multiple revenue streams, while products typically generate just one. Of the

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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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Data streaming with Arka runtime APIs

The Arka runtime is Megh’s data streaming framework. Arka enables applications to build custom data pipelines spanning multiple devices and accelerators. Low-level details are abstracted away by Arka’s resource manager, which maps an application’s pipeline request to the pool of available hardware. This technology enables low-latency, low-overhead data streaming over complex functional topologies through Arka’s

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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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