Progress Report 2019/1/3 PHHung.

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Presentation transcript:

Progress Report 2019/1/3 PHHung

Previous Deep learning … The promised land for CV ? 2019/1/3 Deep learning … The promised land for CV ? Implement convolutional neural network step by step (follow by UFLDL tutorial) Back propagation Algorithm Sparse Autoencoder Softmax Regression Stacked Autoencoders Convolutional neural network

Two direction State of the art algorithm survey 2019/1/3 State of the art algorithm survey 2 hidden layers for pedestrian detection , CVPR 2013 22 hidden layers for 1000+ class object detection , CVPR 2015? Hardware implementation survey Works from 332 TeraDeep from Prudue FPGA & ASIC implement from NYU (LeCun)

A small toy 2019/1/3 Use the building boxes from previous to build a pedestrian detector Train on INRIA pedestrian dataset Test on Caltech pedestrian dataset Classifier + Sliding window Very slow… I should try objectness filter instead of sliding window next time 2 hidden layers (200/100nodes ) Result is approximately at here FPPI~1.2 Miss rate~12.4%

Back to reality… Why we need analysis engine? 2019/1/3 Why we need analysis engine? => To save power consumption For ReSSP : 197mW in VGA , 39.7mW in QCIF For Deep learning engine : HMAX ASIC: 205mW in 256x256 (Neocortical from 332 , ISSCC 12) ConNet FPGA: 10W in 500x375 (NeuFlow from NYU LeCun , CVPR 11) ASIC: 580mW in 500x375 (NeuFlow from Purdue & NYU , MWCAS 12) FPGA: 4W (TeraDeep from Purdue , CVPR 14) None of above can have power lower than 100mW… How about power & detection rate at low resolution (QCIF) ?