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2.5d_rot

The HDL framework for our 2.5D root of trust.

abc

ABC: System for Sequential Logic Synthesis and Formal Verification

accelergy

Accelergy is an energy estimation infrastructure for accelerator energy estimations

ahead

AHEAD: A Tool for Projecting Next-Generation Hardware Enhancements on GPU-Accelerated Systems

bloom

cacti

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

cogdl

CogDL: An Extensive Toolkit for Deep Learning on Graphs

cos598d-serverless

cuda-samples

Samples for CUDA Developers which demonstrates features in CUDA Toolkit

cva6

The CORE-V CVA6 is an Application class 6-stage RISC-V CPU capable of booting Linux

deepspeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

dgl-lifesci

Python package for graph neural networks in chemistry and biology

dgnn

fastertransformer

Transformer related optimization, including BERT, GPT

galois

Galois: C++ library for multi-core and multi-node parallelization

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gem5

This is an read-only mirror of the gem5 simulator. The upstream repository is stored in https://gem5.googlesource.com, code reviews should be submitted to https://gem5-review.googlesource.com/. The mirrors are synchronized every 15 minutes.

gnn-computing

Artifact for PPoPP20 "Understanding and Bridging the Gaps in Current GNN Performance Optimizations"

gpu4gnn

ilang

A Modeling and Verification Platform for SoCs using ILAs

llmcompass_isca_ae

llmcompass_isca_ae_docker

mg-gcn

MG-GCN: Scalable Multi-GPU GCN Training Framework

nccl-tests

NCCL Tests

nicsefc-readme

some docs for rookies in nics-efc

ns3-datacenter

online-softmax

Benchmark code for the "Online normalizer calculation for softmax" paper

osdi21_ae

Artifact for OSDI'21 GNNAdvisor

physical-design-for-nanometer-ics

Assignments of Physical Design for Nanometer ICs (Spring 2017, Prof. Yao-Wen Chang)