Enterprises, system integrators (SIs), OEMs, and developers can take control with Megh’s Open Analytics VAS SDK: A simplified and supported platform for custom, high-performance intelligent video analytics solutions.
VAS SDK allows developers to optimize video (and data) analytics pipelines and integrate highly customizable AI into applications and products.
With AI+ acceleration using GPUs or FPGAs, VAS SDK offers the highest performance, most scalability, greatest flexibility, and best vendor choice for system integrators and independent software vendors.
VAS SDK is a platform optimized for real-world complexity and performance.
Fully customizable pipeline
Open vendor choice
Intelligent video analytics
VAS SDK includes configurable video analytics for several public safety and business operations use cases, and also offers flexibility to integrate other AI inference models and filtering libraries.
For help with creating or implementing video analytics for your custom use case, contact us.
Specifications and implementation
- Languages supported: Python
- AI model frameworks: Pytorch and TensorFlow (TF-Lite)
- Platforms: CPU, GPU, FPGA
- Vendors: Intel, AMD, ARM, Xilinx, NVIDIA
- Cloud infrastructure supported: AWS, Microsoft Azure, Google Cloud
For more technical details, learn about The Megh Platform.
Enhance VAS SDK with our SDK extensions:
- DLE SDK: Train, build, and deploy custom models with Pytorch, TF-Lite, and other frameworks on FPGAs for AI deep learning inferencing
- AFU SDK (coming soon): Develop custom FPGA accelerator function units (AFUs) for analytics using RTL/HLS for deployment on scale-out FPGA platforms
Why VAS SDK?
VAS SDK is an Open Analytics solution, designed to enable flexible, scalable, high-performance, end-to-end integration into your business operations, applications, and products.
- Customize the AI/video analytics pipeline for any camera/sensor stream
- Optimize for accuracy, TCO, or performance based on use case
- Edge-to-cloud deployment options for performance, cost, and regulatory adherence
- Start with CPU and scale to GPU/FPGA as needed
- Change compute platform and/or deployment type on the fly
- Stability for business users while scaling (no need to recode app layer)
Easy AI adoption
- Utilities provided to help report data and optimize analytics
- Concierge Support for custom integration or model development
- Right-size hardware for simple-to-complex use cases, based on pre-tested specs
- Lowest TCO for any performance target
Enables operational reliability
- Intelligent video analytics that actually deliver in complex environments and use cases
This 11-page Dell-EMC technical white paper evaluates a video analytics pipeline with deep learning inference using Megh’s VAS and Intel Programmable Acceleration Card (PAC) FPGAs on Dell EMC PowerEdge R740/R740xd servers.
This 3-page HPE technical white paper discusses how customers can achieve dramatically increased performance and reduced TCO by running the Megh Computing real-time streaming video analytics platform on the HPE ProLiant DL380 Gen10 server with Intel Arria 10 GX FPGAs compared to a configuration without FPGAs.
In the following video, see how Intel and Megh have partnered in developing low-latency analytics using AI with Intel FPGAs.