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.