CNN AI Accelerator using DARKNET-19 YOLO v3 Tiny

Our GOAL is to design HW AI accelerator that works low power, high performance.

Algorithm: DARKNET-19 AI Model: YOLO v3 Tiny Boards: PYNQ-Z2 FPGA Borad Main tool: Xilinx Vivado, Vitis AI

Developing

Quantization DARKNET-19 parameters to INT8 type.
Coding reference code in python. (all parameters are called in txt files.)
Design Conv layer.
Design Pooling layer.

Substep

Quantization DARKNET-19 parameters to INT8 type.

My team discuss about Bacth Normalization.
- For Implementation, We guess Bacth Norm is not essential & causes large HW resources.
- That discussion would be great paper subjects.

Coding reference code in python. (all parameters are called in txt files.)

That isn't too hard but we didn't get parameter txt files written by INT8 type.
OpenCV, TensorFlow, Numpy, etc... will be used.
https://github.com/ValentinFigue/TinyYOLOv3-PyTorch : developed the model from their work.
https://github.com/openvinotoolkit/open_model_zoo : converted weight file from their work.

Design Conv layer.

The components of Conv layer is "Convolution", "BatchNorm", and "Leaky ReLU".
Convolution layer
- almost done.
- top module and controller are remain.
BatchNorm
- For HW resources problem, we determine delete BatchNorm layer.
- all parts would be done and have problem, we will apply this layer.
Leaky ReLU
- ReLU vs Leaky ReLU
- One of our issues.

Design Pooling layer.