With the advent of IoT and the proliferation of connected embedded devices, one of the biggest challenges in developing competitive IoT solutions is the ability to bring intelligence at the Edge of the IoT networks. Edge computing is crucial in IoT applications as it paves the way for faster real-time inference by embedding computation capability in on-premise infrastructure resulting in a dramatic improvement in overall system reliability and performance.
With edge computing increasingly forming the foundation of next-generation secure and connected devices, it is important to highlight the significance played by the hardware accelerators in determining system performance & efficiency and therefore should be considered with utmost importance while developing edge gateway solutions.
Over the years significant advancement in FPGA technology has led to FPGAS becoming mainstream for developing intelligent edge platforms. FPGAs sophisticated performance and adaptability coupled with their ability to deliver the highest throughput at the lowest latency makes them ideal for enabling highly responsive real-time inference at the edge.
At iWave Systems, a leading FPGA design house based in Bangalore, we have expended state of the art Xilinx Zynq® UltraScale+™MPSoC FPGA modules to bring forth intelligence in edge devices using advanced AI/ML accelerations. iWave’s Zynq® UltraScale+™MPSoC FPGA SOM offers versatile hardware accelerations for intuitive deployment of functions such as, image /speech recognition, object /pose detection, etc. and a flexible platform that enables developers to continually refine features and sharpen their competitive edge. Implementing artificial neural networks in FPGAs provides the flexibility to adapt applications with changing standards and end-user demands, which in turn future proofs your designs.
iWave also provides comprehensive Zynq® UltraScale+™MPSoC development platforms for an immediate evaluation of AI/ML applications.