RealTime Object Detection on 640x480 Image With VGG16SSD Convolutional neural networks (CNNs) show high performance in computer vision tasks including object detection, but a lot of weight storage and computation requirement prohibits realtime processing, 30 frames per second (FPS). This demonstration will show an CNN accelerator that can process realtime object detection on the 640x480 image. A high performance, complex CNN was implemented, singleshot multibox detector (SSD) with VGG16. The number of weights is reduced by a pruning scheme. For the higher utilization of operators, the acceleratoraware pruning was applied. The weights of the pruned network can be entirely stored in the internal memory. The proposed design reaches 42 FPS on XC7VX690T FPGA, showing VOC07 test mAP of 78.13%.

Implementation of Yolov2 Image Recognition and Other Testbenches for a CNN Accelerator An implementation of Yolov2 image recognition and other testbenches for a deep learning accelerator is presented. This chip is the initial version of our ongoing effort for a higher performance accelerator development. The accelerator is based on a systolic array and can handle convolution and maxpooling layer in a combined way or separately using 16 bit floatingpoint data. It also supports innerproduct and LSTM layers. For demonstration and as one of the design verification testbenches, we implemented Yolov2 image recognition for 80 object classes. We converted the Yolov2 software to 16 bit floating point version and used it in the simulation and FPGA experiment during the chip development. Several other testbenches were designed and used to test various networks. In this paper, the acceleratorxe2x80x99s architecture and the Yolov2 implementation on our FPGA board including camera and LCD is explained, including the software implementation. Some analyses on the measured and estimated performance is shown.