Pinned Repositories
donghn.github.io
intel-caffe-old
micronet
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape
QNet
scale-sim-v2
Repository to host and maintain scale-sim-v2 code
SoftMC
SoftMC is an experimental FPGA-based memory controller design that can be used to develop tests for DDR3 SODIMMs using a C++ based API. The design, the interface, and its capabilities and limitations are discussed in our HPCA 2017 paper: "SoftMC: A Flexible and Practical Open-Source Infrastructure for Enabling Experimental DRAM Studies" <https://people.inf.ethz.ch/omutlu/pub/softMC_hpca17.pdf>
Spiking_Neural_Network_Conversion
stonne
STONNE: A Simulation Tool for Neural Networks Engines
transformer
Implementation of "Attention Is All You Need" using pytorch
donghn's Repositories
donghn/intel-caffe-old
donghn/donghn.github.io
donghn/micronet
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape
donghn/QNet
donghn/scale-sim-v2
Repository to host and maintain scale-sim-v2 code
donghn/SoftMC
SoftMC is an experimental FPGA-based memory controller design that can be used to develop tests for DDR3 SODIMMs using a C++ based API. The design, the interface, and its capabilities and limitations are discussed in our HPCA 2017 paper: "SoftMC: A Flexible and Practical Open-Source Infrastructure for Enabling Experimental DRAM Studies" <https://people.inf.ethz.ch/omutlu/pub/softMC_hpca17.pdf>
donghn/Spiking_Neural_Network_Conversion
donghn/stonne
STONNE: A Simulation Tool for Neural Networks Engines
donghn/transformer
Implementation of "Attention Is All You Need" using pytorch