Machine Learning (ML) is becoming a fundamental part of almost any computer vision-based application on the edge. From pedestrian detection in ADAS to cancer diagnosis in medical, and quality assurance in agriculture. However, there are challenges involved in developing an optimized and high-precision machine learning applications on the edge such as selecting the right processing system and neural network. FPGAs, as edge computing units, have shown a solid potential on improving the performance of ML applications. Aldec has recently developed DNN-based object detection applications on TySOM-3A-ZU19EG embedded development board (using Xilinx® Zynq™ MPSoC™ FPGA) for its customers to kick start their ML projects. In this webinar, you will learn about the ML application development process and what tools are required to simplify the design and implementation for FPGA-based machine learning applications.
AI vs machine learning vs deep learning
Deep learning application development flow and its challenges
ASICs vs GPUs vs FPGAs - which one is better for deep learning applications?
Main methods to achieve high-performance machine learning applications
How to develop an DNN-based object detection application using Xilinx Zynq MPSoC FPGA on TySOM embedded development board
Farhad Fallahlalehzari works as an Application Engineer at Aldec focusing on Embedded System Design. He provides technical support to customers developing embedded systems. His background is in Electrical and Computer Engineering, concentrating on Embedded Systems and Digital Systems Design. He received his Masters of Science in Electrical Engineering from the University of Nevada, Las Vegas in 2016. He completed his Bachelors of Science in Electrical Engineering at Azad University, Karaj Branch, Iran in 2013.
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