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TRACKING SOCIAL DISTANCE:
WHAT ENABLES THOSE RED AND GREEN BOXES?

Tracking Social Distance: What Enables Those Red And Green Boxes?

Technology jargon such as machine learning, artificial intelligence has become a household name due to the extensive reporting of COVID-19 on various Media highlighting the importance given by various governments to safeguard their citizen. Everywhere you see video feed full of red and green circles or boxes identifying various objects, mostly humans.

Is artificial intelligence just about it? Well, the technology world has gone way ahead of making those boxes in real-time streaming and the technologists are grappling with advanced challenges to make it a deployment-ready solution that doesn’t infringe individual privacy. However, the tech journey so far has been nothing short of a sci-fi battle between re-programmable FPGAs and graphics genius GPUs.

The epic battle that ensued between programmable logic FPGA and GPU players to win the coveted crown of the best efficient hardware platform is still on. However, the FPGAs seem to win wider adoption with remarkable improvements in power consumption while keeping up with challenging accuracy demands of Deep Neural Network (DNN) apps.

Xilinx research shows its Ultrascale+TM XCVU13P FPGA (38.3 INT8 TOP/s) platform provides the same computing power as Tesla P40 (40 INT8 TOP/s) but flexibility and on-chip memory on Xilinx device results in significantly higher computing capability for different workloads and apps. Another Xilinx research in a general-purpose compute efficiency shows its Virtex Ultrascale+ performing 4x better than Nvidia Tesla V100. The scales tilt further in favor of FPGAs when we consider functional safety demanded by safety-critical aviation, autonomous automotive, and defense applications, such as ADAS.

Embedded Vision requires the machines which have the ability to see, sense, and quickly respond to challenges the hardware designers create next-gen architecture that is highly differentiated and extremely responsive to adapt to ever-evolving algorithms and image sensors.

If the ones mentioned above are computing power-intensive use-cases that need to utilize custom neural networks (CNNs/DNNs), the other side of the spectrum requires extremely low-power operations with a flexible solution building approach.

In pursuit of maximizing the efficiency of machines to achieve higher throughput of operations, the Industrial Internet of Things (IIoT) is driving Industry 4.0. Such applications require a combination of software programmability coupled with real-time processing of sensor data to leverage any-to-any connectivity in a secure and safe manner. The flexible nature of FPGA programmability and low-power consumption make FPGAs a perfect choice for Industry 4.0 solutions.

It is just the beginning of how FPGA platforms are powering the ML solutions that are likely to see mass-adoption and become household essentials as we create diverse use-cases to help people, businesses, and governments to make the world a safer and smarter place

Know how Softnautics can help you design FPGA-Powered ML solution for your use-case.

About Author: Prasant Agarwal

Prasant is leading the Marketing team at Softnautics. He has 15+ years of experience in developing cutting-edge multimedia and connectivity products for STMicroelectronics, Samsung, and Solarflare Communications (Now Xilinx) and led corporate rebranding for Persistent Systems. Leveraging his technology domain and experience, he is now focusing on enabling technology buyers to make the right business choices by bridging business challenges and best-fit technology solutions.