?????! ?? ?? ???? AI? ????? ??? ?? ??? ??? ????? NVIDIA GTC 2023? 3? 20?(?)?? 3? 24?(?)?? ????? ??? ?????.
GTC? ???, ???, ???? ??? ??? ?????? ???, ??? ????? ???? ????? ???? ?? ? ?? ?? ???? ?? ? ?? ???? ??????.
650? ??? ??? ?? AI, HPC, ????, ????, ?? AI ? ??? ??? ????? ? ?? ?? ??? ? ???? ???? ?? ??? ?? ???? ? ????. ??? ??? ?? ??? ?? ???, ???? ??, ???? ?? ?? NVIDIA ??? ??? ??? ?? ??? ???? ????.
?? ?????? ?? ??? ?? ?? Solution Architect ??? ?? ??? Must Watch : GTC ??? ?? ??? ??????.
S52226 – GTC 2023 Keynote – Jensen Huang: Founder and CEO, NVIDIA (??? ?? ??)
GTC? ??? NVIDIA ?? ? CEO & Founder? ?? ?? ??? ???? ???? ?????. 3? 21?(?) ? 12?? ????? ?? ??? ?? ?? ???? ? ????. GTC ?? ?????? ????? ???? ??? ??? ?? ????. (21?(?)?? 22?(?)? ???? ??)
LLM(Large Language Model) & ???AI ?? ?? ??
| S52092 | Fireside Chat with Ilya Sutskever and Jensen Huang: AI Today and Vision of the Future * Ilya Sutskever: Co-founder and Chief Scientist, OpenAI * Jensen Huang: Founder and CEO, NVIDIA |
| S52089 | Generative AI Demystified |
| S51930 | Deep Learning Demystified |
| S51831 | Using AI to Accelerate Scientific Discovery * Demis Hassabis: Founder and CEO, DeepMind |
| SE52185 | Korea AI day : AI Developer Meetup (?????) |
| DLIW52108 | [??] ??? ??? : ?? GPU? ??? ? ?? ?? ???? ?? (?????) |
| DLIW52085 | [??] ?? ??? : ?? ??? ?? ???? ?? ? ?? (?????) |
| S52422 | Compile and Train with 43% Speedup using PyTorch 2.0 |
| S51935 | How to Efficiently Build and Deploy Large Language Models |
| S51179 | How to Assess and Mitigate Bias in AI Models |
| S51088 | Efficient Inference of Extremely Large Transformer Models |
| S51196 | FasterTransformer v6.0: New Features and Beyond |
| S52166 | FP8 for Deep Learning |
| S51370 | FP8 Mixed-Precision Training with Hugging Face Accelerate |
| S51393 | FP8 Training with Transformer Engine |
| S51111 | Scaling Deep Learning Training: Fast Inter-GPU Communication with NCCL |
| S51800 | Scaling Large Language Model Training with PAX on GPUs |
| S51151 | Taming LLMs with the Latest Customization Techniques |
| S51703 | Accelerating Disentangled Attention Mechanism in Language Models |
| S51226 | Accelerating Large Language Models via Low-Bit Quantization |
| S51158 | Accelerating Transformer-Based Encoder-Decoder Language Models for Sequence-to-Sequence Tasks |
| S51185 | Automated Bias Testing of Language Models |
| S51482 | Colossal-AI: Scaling AI Models in the Big Model Era |
| S51947 | Leveraging Large Language Models for Generating Content |
| CWES52026 | Connect with the Experts: Accelerating and Deploying Deep Learning Models to Production |
| S51486 | How to use Generative AI to Build Content for Real-World Applications |
| S51945 | Improving Dense Text Retrieval Accuracy with Approximate Nearest Neighbor Search |
| S51422 | SwinTransformer and its Training Acceleration |
Digital Twin ?? ?? ??
| S52163 | 3D by AI: Using Generative AI and NeRFs for Building Virtual Worlds |
| S51239 | 3D Art Goes Multiplayer: Behind the Scenes of Adobe Substance’s “End of Summer” Project with Omniverse |
| S51663 | 3D Synthetic Data: Simplifying and Accelerating the Training of Vision AI Models for Industrial Workflows |
| S52057 | Building Tools for the Metaverse on Omniverse Kit |
| SE52117 | 3D and Beyond: How 3D Artists Can Build a Side Hustle Building Digital Twins and SimReady Assets |
| S51676 | AI Fundamentals for Building Intelligent, Interactive Digital Humans |
| SE52348 | Developer Breakout: Building Extensions and Apps on Omniverse |
| DLIT52362 | Fundamentals of Universal Scene Description* |
| DLIT52624 | How to Build Custom 3D Scene Manipulator Extensions with NVIDIA Omniverse* |
| DLIT51931 | Synthetic Data Generation for Training Object Detection Models* |
| S51754 | Unlocking AI to Build the Metaverse |
| S51344 | How General Motors Explores Future Automated Workflows Using a Single ‘Live’ USD Dataset with Omniverse |
| S51090 | Practical Use Case of Reinforcement Learning and Sim-to-Real Transfer for Manufacturing |
| S51494 | Creating Digital Twins and Simulations of Industrial Robotic Workcells for Smart Factories |
| S51520 | Driving the Future of Smart Factories With Cloud-enabled Digital Twins |
| S51533 | How to Create Modern, Delightful and Sustainable 3D Design by integrating Modeling, Simulation and Product Experiences – Empowered by GPU Computing |
| S51626 | Welcome to the Open-verse: Connecting Open Platforms through Omniverse |
HPC ?? ?? ??
| S52137 | Advances in Accelerated Computing for Scientific Computing |
| S51075 | Defining the Quantum-Accelerated Supercomputer |
| S51358 | A Balanced View of Climate-Change Data Production and User Requirements through Technological Developments |
| S51074 | A Deep Dive into the Latest HPC Software |
| S51840 | AI Driven Atomistic Simulation For Materials and Reactive Chemistry |
| S51997 | An Update on the Alps Infrastructure: Opportunities for Weather and Climate Modeling |
| S51272 | Applying Large Language Models and HPC in a Workflow to Predict New COVID Variants |
| S51462 | Borrowing from Astronomy to Improve Deep Learning Weather Prediction |
| CWES52130 | Connect with the Experts: GPU-Accelerated Quantum Chemistry and Molecular Dynamics |
| S51110 | Cutting-Edge CUDA Technologies for Molecular Dynamics and Beyond |
| S51610 | Learn How Tata Steel Gained Over 23x of Speedup in Discrete Element Modeling Simulation Leveraging NVIDIA CUDA Software |
| S52574 | Optimizing Formula One with Computational Fluid Dynamics |
| S51204 | Transforming Clouds to Cloud-Native Supercomputing: Best Practices with Microsoft Azure |
Semiconductor ?? ?? ??
| S52510 | Accelerating Computational Lithography: Enabling our Electronic Future |
| S51795 | Enabling the Deep Ultraviolet-to-EUV Transition in Advanced Semiconductor High-Volume Manufacturing with Deep Learning |
| S51041 | High-Volume EUV Mask Inspection with Synthetic Data Generation |
| S51040 | Improving Time-to-Results of Electronic Systems using GPU-Accelerated Circuit Simulation |
DPU ?? ?? ??
| S52382 | vSphere on DPUs Behind the Scenes: A Technical Deep Dive (Presented by VMware Inc.) |
| S51745 | Accelerating MPI and DNN Training Applications with BlueField DPUs |
| S51644 | Critical Use Cases for NVIDIA DPUs in Modern Private Cloud Infrastructures |
| S52156 | A Bridge to 6G – Aerial Research and Innovation Platform |
??? ?? ?? ? ??? ?? ???? ???? ?? ??????. ?? ??? ???? ?????? ???, ??? ????, NVIDIA? ???? ??? ??? ??? ???? ? ??? ??? ? ? ???? ??? ? ?? ??? ? ????.
GTC ?? ?? ???
- Technology top sessions
- Developer top sessions
- Omniverse top sessions
- Drive Dev Day
- Business leader top sessions
- Cloud top sessions
- Research top sessions
- Data Science top sessions
- Healthcare top sessions
- Cybersecurity top sessions
- General Top sessions for Startups
- VC top sessions for Startups
- GenAI top sessions for Startups
- Omniverse top sessions for Startups
3? 24?(?)? ???? ?? ?? ???? ?? Korea AI Day ??? ????? ? ??? ??????. ?? AI, Omniverse, LLM (Large Language Model), ???? ?? ? ??? AI ?? ? ????? ???? ??? ?? ?? ??? ?? ??? ? ??? AI ?? Inception ???? ???? ??? ???? ???? ????. ?? Korea AI Day ?????? ???? ?? ?? ?? ? ??? ??????.
?? GTC 2023 ?? ???? ??? ?? ??? ???? GTC Korea Team (GTC_Korea@nvidia.com)? ????? ???????.
?? GTC?? ??? ???? ?????!
