Yonghao-Tan

Yonghao-Tan's Stars

• usyd-fsalab/fp6_llm

  An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).

  Language:Cuda14812

• xijiu9/Train_Transformers_with_INT4

  Language:Python1274

• nbasyl/LLM-FP4

  The official implementation of the EMNLP 2023 paper LLM-FP4

  Language:Python1457

• maestro-project/maestro

  An analytical cost model evaluating DNN mappings (dataflows and tiling).

  Language:MATLAB17055

• kotarot/rectangle-packing-solver

  A solver to find a solution of the 2D rectangle packing problem by simulated annealing (SA) optimization.

  Language:Python8017

• sangyc10/CUDA-code

  Language:Cuda50061

• GATECH-EIC/ShiftAddViT

  [NeurIPS 2023] ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer

  Language:Python27

• GeoMSK/FiducciaMattheyses

  Language:Python15

• Bruces1998/FM_algorithm

  Language:Python1

• facebookresearch/deit

  Official DeiT repository

  Language:Python3.9k547

• google-research/albert

  ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

  Language:Python3.2k571

• HuangOwen/Awesome-LLM-Compression

  Awesome LLM compression research papers and tools.

  84448

• PingchengDong/GQA-LUT

  The official implementation of the DAC 2024 paper GQA-LUT

  Language:Python9

• state-spaces/mamba

  Mamba SSM architecture

  Language:Python11.4k923

• kyegomez/VisionMamba

  Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images

  Language:Python29614

• LiheYoung/Depth-Anything

  [CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation

  Language:Python6.3k480

• zslwyuan/Basic-SIMD-Processor-Verilog-Tutorial

  Implementation of a simple SIMD processor in Verilog, core of which is a 16-bit SIMD ALU. 2's compliment calculations are implemented in this ALU. The ALU operation will take two clocks. The first clock cycle will be used to load values into the registers. The second will be for performing the operations. 6-bit opcodes are used to select the functions. The instruction code, including the opcode, will be 18-bit.

  Language:Verilog11131

• DAMO-NLP-SG/Video-LLaMA

  [EMNLP 2023 Demo] Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

  Language:Python2.6k235

• graphdeco-inria/gaussian-splatting

  Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"

  Language:Python12.4k1.5k

• princeton-vl/DROID-SLAM

  Language:Python1.7k273

• cvg/nice-slam

  [CVPR'22] NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

  Language:Python1.4k191

• yenchenlin/nerf-pytorch

  A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

  Language:Python5.2k1k

• facebookresearch/fairseq

  Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

  Language:Python29.7k6.3k

• dlaptev/RobustPCA

  Robust PCA implementation and examples (Matlab)

  Language:Matlab19074

• Buck008/Transformer-Accelerator-Based-on-FPGA

  You can run it on pynq z1. The repository contains the relevant Verilog code, Vivado configuration and C code for sdk testing. The size of the systolic array can be changed, now it is 16X16.

  Language:Verilog803

• chihhuiho/yoro

  Language:Python16

• VainF/DeepLabV3Plus-Pytorch

  Pretrained DeepLabv3 and DeepLabv3+ for Pascal VOC & Cityscapes

  Language:Python1.8k424

• chiragsakhuja/spotlight

  Language:MATLAB112

• GATECH-EIC/ViTCoD

  [HPCA 2023] ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design

  Language:Python839

• bmartini/zynq-axis

  Hardware, Linux Driver and Library for the Zynq AXI DMA interface

  Language:Verilog9638