房彪's repositories
Neural-Network-Compression-and-Accelerator-on-Hardware
My name is Fang Biao. I'm currently pursuing my Master degree with the college of Computer Science and Engineering, Si Chuan University, Cheng Du, China. For more informantion about me and my research, you can go to [my homepage](https://github.com/hisrg). One of my research interests is architecture design for deep learning and neuromorphic computing. This is an exciting field where fresh ideas come out every day, so I'm collecting works on related topics. Welcome to join us!
SNPE
Snapdragon Neural Processing Engine (SNPE) SDKThe Snapdragon Neural Processing Engine (SNPE) is a Qualcomm Snapdragon software accelerated runtime for the execution of deep neural networks. With SNPE, users can: Execute an arbitrarily deep neural network Execute the network on the SnapdragonTM CPU, the AdrenoTM GPU or the HexagonTM DSP. Debug the network execution on x86 Ubuntu Linux Convert Caffe, Caffe2, ONNXTM and TensorFlowTM models to a SNPE Deep Learning Container (DLC) file Quantize DLC files to 8 bit fixed point for running on the Hexagon DSP Debug and analyze the performance of the network with SNPE tools Integrate a network into applications and other code via C++ or Java
Onnx-python
This repository is Onnx tutorial summary for python implements , which comes from other web resource.
Face-Tracking-Robot
This project introduces how to build a Face Tracking Robot Using Some components of Hardware and Software, and Algorithm, Please View more detail as below if you want.
Windows-universal-samples
API samples for the Universal Windows Platform.
CNN_for_SLR
A trained Convolutional Neural Network implemented on ZedBoard Zynq-7000 FPGA.
incubator-singa
Mirror of Apache Singa (Incubating)
Deep-Compression-PyTorch
PyTorch implementation of 'Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding' by Song Han, Huizi Mao, William J. Dally
Parametric-Leaky-Integrate-and-Fire-Spiking-Neuron
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks