We have did multiple implementation of Machine Learning and Neural Networks on FPGA [including VCU1525, Alveo, ZCU102, ZCU104 and Ultra96].
See our Machine Learning Portfolio: LogicTronix[dot]com_ML_Portfolio
Some of our Machine Learning/DNNDK based FPGA Implementations are:
We have developed the “real time vehicle counting system” based on camera feed or video stream of real traffic video. Our system can be implemented with low cost MPSoC board [Ultra96 FPGA] or other custom board with MPSoC.
Watch the demo of “Vehicle Counting System”: YouTube Link
This implementation uses the YoloV2 algorithm for object recognition, it is implemented on VCU1525 FPGA device on the Nimbix Cloud Platform.
We also have developed different applications based on ML Suite for Alveo FPGA [U200] card. For exploring with ML Suite for Alveo, ML Suite, there is also an example of image classification using the Googlenet with kernel precision INT8, INT16 for test classify and batch classify. This acceleration run on Alveo U200 as well as U250 [with some revision].
Here is the Video Tutorial Link: Machine Learning Suite Acceleration on Alveo FPGA-Video Tutorial. If you need any reference document or support on it then you may contact us!
This application is developed for implementing the DNNDK on the ZCU104 using the PG338 of Xilinx[Deephi]. This implementation is used for Image Classification and Face Detection application with some other application.
It is DNNDK implementation on the Ultra96 FPGA for Image Classification and Face Detection.
YOLO-V3 tiny [caffe] for Object Detection with DPU-DNNDK and Ultra96 FPGA. This implementation convert the YOLOv3 tiny into Caffe Model from Darknet and implemented on the DPU-DNNDK 3.0 version.
It is generating 30+ FPS on video and 20+FPS on direct Camera [Logitech C525] Stream. Goto tutorial: Yolov3-tiny-on-DNNDK-by-LogicTronix
We have used the BNN for digit recognition and vehicle number plate recognition, QNN/CNN for image classification and few other NN/ML algorithm are used for other applications as traffic sign detection, object recognition etc.
If you are interested on implementing Machine Learning algorithms or Neural Networks on you application or custom application then you can contact us at: email@example.com or firstname.lastname@example.org.