I am interested to work on a long term research project where I need to find a new robust method (Approach) in the area of Resource Constrained Devices with Machine/Deep Learning for memory optimization, algorithm optimization, deep compression using pruning and quantization. I am open to use Arm Cortex, ESP32, or an FPGA for hardware acceleration. These embedded system have limited power, memory, and etc... for Deep Learning implementation.
Therefore, I need help to find a gap in this area to establish a work.
I have five year of experience in zynq based FPGA and ESP32 controller board usinf Verilog/SV, C/C++(HLS and ARM) and python/TCL-TK scripting language. I have experience in machine learning algorithms: back propogation Daha Fazla
Bu iş için 15 freelancer ortalamada $35/saat teklif veriyor
Hello, This sounds like an interesting project and I'm sure I can help. I'd love to speak with you in person to discuss your requirement in detail. Please contact me for an unbinding conversation.
Hi, I wish you are going well. I am interested in your ML CV hardware project. I prefer to use FPGA for hardware acceleration of CV algorithms. I need to know more details. Please contact me, then I will help you. Than Daha Fazla
I have 15 years of exprience in embedded area. I think Nvidia Jetson would work fine for you. I have worked with Jetson nano. If you want to share more information contact me please
I have done Masters degree in Microelectronics from one of the best UK university. Recently I worked with a chip design having SPI, Ethernet, UDP, GSE and antenna design. I used system Verilog UVM For test bench. From Daha Fazla