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travisdai's Projects

ai-chip icon ai-chip

A list of ICs and IPs for AI, Machine Learning and Deep Learning.

ai-matrix icon ai-matrix

To make it easy to benchmark AI accelerators

alpa icon alpa

Auto parallelization for large-scale neural networks

altsplit icon altsplit

An MPI-based distributed model parallelism technique for MLP

blackcat_tensors icon blackcat_tensors

Matrix-Vector Library Designed for Neural Network Construction. cuda (gpu) support, openmp (multithreaded cpu) support, partial support of BLAS, expression template based implementation PTX code generation identical to hand written kernels, and support for auto-differentiation

bulk icon bulk

A modern interface for implementing bulk-synchronous parallel programs.

cacti icon cacti

An integrated cache and memory access time, cycle time, area, leakage, and dynamic power model

cacti-6.0 icon cacti-6.0

ASIC simulation of Multi-ported Memory Module. And it can offer SRAM-based dual-port basic building block to support multiple read/write ports, and it can easily get the simulation result (like frequency, power consumption, and area... etc.).

camuy icon camuy

Fast evaluation of CNNs on configurable systolic arrays based on abstract metrics

casim icon casim

Computer Architecture Simulator with ZSim

chatgpt_academic icon chatgpt_academic

科研工作专用ChatGPT拓展,特别优化学术Paper润色体验,支持自定义快捷按钮,支持自定义函数插件,支持markdown代码块表格显示,Tex公式双显示,新增Python/C++项目剖析/自译解功能,PDF/LaTex论文翻译/总结功能,兼容ChatGLM等本地模型

chipyard icon chipyard

An Agile RISC-V SoC Design Framework with in-order cores, out-of-order cores, accelerators, and more

clacc icon clacc

Deep Learning Accelerator (Convolution Neural Networks)

cranium icon cranium

🤖 A portable, header-only, artificial neural network library written in C99

dataanalysisinaction icon dataanalysisinaction

(已完结)《极客时间数据分析实战45讲-详细笔记》包含markdown、图片、思维导图、代码 、数据。 可直接阅读代码、测试!

deeplearning icon deeplearning

Deep Learning introduction and its application in various fields

deeplite-profiler icon deeplite-profiler

A collection of metrics to profile a single deep learning model or compare two different deep learning models

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