Megh and Intel® performed a performance and validation assessment of Megh Video Analytics Solution (VAS) running on Intel® NUC kits with 11th or 12th generation Intel® Core™ processors. The kits are cost-effective, small-form factor hardware that provide customers with the performance, power, and accuracy they need to run Megh VAS at the edge. Results demonstrate
The explosion of smart devices, high-bandwidth networks like 5G, and the need to capture and discern data closer to the source have led to the need for a unified edge-analytics application development framework. Devices at the edge, however, are diverse, with each providing their own optimized framework. This makes application portability a big challenge. What
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
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
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
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
We are excited to announce that the Megh Video Analytics Solution (Megh VAS) Beta 1 Release is available! The Megh VAS Beta 1 Release extends the earlier Megh VAS alpha releases. The alpha releases showcased inline processing of various streams, including filtering and transformations of data. For example, the Megh VAS Alpha Release for Intel
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