Pinned Repositories
basic_verilog
Must-have verilog systemverilog modules
Brain-Cog
Brain-inspired Cognitive Intelligence Engine (BrainCog) is a brain-inspired spiking neural network based platform for simulating the cognitive brains of different animal species at multiple scales and realizing brain-inspired Artificial Intelligence. The long term goal of BrainCog is to provide a comprehensive theory and system to decode the mechanisms and principles of human intelligence and its evolution, and develop artificial brains for brain-inspired conscious living machines in future human-machine society.
CppCoreGuidelines-zh-CN
Translation of C++ Core Guidelines [https://github.com/isocpp/CppCoreGuidelines] into Simplified Chinese.
discodiffusion
MLP_NeuroSim_V3.0
Benchmark framework of synaptic device technologies for a simple neural network
Real-Time-Voice-Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
Unet
vits
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
axyre's Repositories
axyre/Real-Time-Voice-Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
axyre/basic_verilog
Must-have verilog systemverilog modules
axyre/Brain-Cog
Brain-inspired Cognitive Intelligence Engine (BrainCog) is a brain-inspired spiking neural network based platform for simulating the cognitive brains of different animal species at multiple scales and realizing brain-inspired Artificial Intelligence. The long term goal of BrainCog is to provide a comprehensive theory and system to decode the mechanisms and principles of human intelligence and its evolution, and develop artificial brains for brain-inspired conscious living machines in future human-machine society.
axyre/CppCoreGuidelines-zh-CN
Translation of C++ Core Guidelines [https://github.com/isocpp/CppCoreGuidelines] into Simplified Chinese.
axyre/discodiffusion
axyre/MLP_NeuroSim_V3.0
Benchmark framework of synaptic device technologies for a simple neural network
axyre/Unet
axyre/vits
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech