Nvidia GTC

Last updated
Nvidia GTC
Date(s)March & November (4–5 days)
FrequencySemi-annual
Venue San Jose Convention Center
Location(s) San Jose, California, U.S.
FoundedOctober 2, 2009 (2009-10-02)
Most recentMarch 18, 2024 (2024-03-18)
Next eventMarch 17–20, 2025 (2025-03-17 – 2025-03-20)
Attendance75,358 (est.)[ when? ][ citation needed ]
Organized by Nvidia
Website www.nvidia.com/gtc/

Nvidia GTC (GPU Technology Conference) is a global artificial intelligence (AI) conference for developers that brings together developers, engineers, researchers, inventors, and IT professionals. [1] Topics focus on AI, computer graphics, data science, machine learning and autonomous machines. Each conference begins with a keynote from Nvidia CEO and founder Jensen Huang, followed by a variety of sessions and talks with experts from around the world.

Contents

It originated in 2009 in San Jose, California, with an initial focus on the potential for solving computing challenges through GPUs. [2] In recent years, the conference focus has shifted to various applications of artificial intelligence and deep learning, including: self-driving cars, healthcare, high performance computing, professional visualization, and Nvidia Deep Learning Institute (DLI) training. [3]

History

GTC 2018 attracted over 8,400 attendees. Due to the COVID pandemic of 2020, GTC 2020 was converted to a digital event and drew roughly 59,000 registrants. The 2021 GTC keynote, which was streamed on YouTube on April 12, included a portion that was made with CGI using the Nvidia Omniverse real-time rendering platform. Due to the photorealism of the event, including a model of CEO Jensen Huang, news outlets reported not being able to discern that a portion of the keynote was CGI until later revealed in a blog post on August 11. [4]

Event Almanac
YearDatesLocationNotable speakersAnnouncements
2009Sep 30–Oct 2 San Jose, CA Jensen Huang; Richard Kerris; Jon Peddie; Hanspeter Pfister, Harvard University Fermi microarchitecture; [5] [6] Maybe first keynote ever in 3D; double precision n body simulation demo
2010Sep 20–23 San Jose Convention Center, San Jose, CA Jensen Huang; Sebastian Thrun, robotics at Stanford and engineer at Google; Klaus Schulten, computational biologist, Univ. of Illinois, Urbana-ChampaignDX11 Tessellation; Iray on 3DSMax; CUDA x86; Matlab CUDA Accelerated Parallel Computing Toolbox; CUDA roadmap revealed through Maxwell; Quadro graphics cards for video gaming [7]
2011Dec 14–15China [8] Jensen Huang CUDA [9] [10]
2012May 14–17San JoseJensen Huang; Iain Couzins (Human Brains and Crowd Behavior), and Part Time Scientists Robert Boehme and Wes Faler (Space) Kepler microarchitecture; [11] GeForce Grid (GeForce Now) [12]
2013Mar 18–21San JoseJensen Huang; Erez Lieberman Aiden (genomics pioneer), Ralph V. Gilles (President and CEO of SRT Brand at Chrysler)Face Works for facial animation [13]
2014Mar 25San JoseJensen Huang; Dirk Van Gelder; Danny Nahmias, Adam Gazzaley; Oculus CEO Brandon (announced Facebook was acquiring)NVLink; [14] Pascal microarchitecture; [15] Tegra mobile; [16] Audi drives itself onto stage
2015Mar 17–20San JoseJensen Huang; Elon Musk; Jeff Dean; Andrew Ng; Andrej Karpathy (director of AI/Computer Vision at Tesla) Nvidia Drive; Titan X; [17] Voice recognition [18]
2016Apr 4–8; Sep 28–29San Jose; AmsterdamJensen Huang Pascal microarchitecture new version; [19] DGX-1; Nvidia Drive PX2; iRay; DGX-2
2017May 8–11San Jose; Europe; Israel; JapanJensen HuangVolta Supercomputer; [20] ISAAC Robot Simulator [21]
2018Mar 26–29San Jose; Europe; Israel; Japan Jensen Huang Clara for healthcare and biomedical research; [22] ARM partnership announce for IoT; [23] RAPIDS Demo [24]
2019Mar 17–21San Jose; Europe; Israel; JapanJensen HuangGauGAN for animation; [25] Orin auto AI processor; [26] Self-driving car partnership with Toyota; [27] CUDA-X AI acceleration libraries adopted by PayPal, SAS, Walmart and Microsoft [28]
2020Oct 5–9Digital [29] Jensen HuangAI Supercomputer for Biomedical Research; [30] Ampere GPUs for visual computing; [31] A100; [32] Artificial Intelligence for Edge and Cloud; [33] ISAAC Demo [34]
2021Apr 12–16DigitalJensen Huang; Geoffrey Hinton; Yann LeCun; Yoshua Bengio [35] Grace; [36] [37] BMW Virtual Factory; [38] Omniverse Enterprise; [39] SDK for quantum simulations; [40] DGX SuperPOD; [41] Nvidia BlueField 3 DPU [42]
2022Mar 21–24DigitalJensen Huang; Andrew Ng, Dale Durran, Doruk Sonmez Hopper architecture, [43] H100 GPU, [44] Jetson AGX Orin [45]
2022Sep 19–22DigitalJensen Huang; others speakers to be announcedTBA
2023Mar 20–23DigitalJensen Huang;

Valerie Taylor, Director, Mathematics and Computer Science Division, Argonne Distinguished Fellow, Argonne National Laboratory;

Demis Hassabis, Founder and CEO, DeepMind;

Ilya Sutskever, Co-founder and Chief Scientist, OpenAI;

Anima Anandkumar, Senior Director of ML Research, Nvidia;

Scott Belsky, Chief Strategy Officer and EVP, Design and Emerging Products, Adobe;

Kathy Smith, Artist and Professor School of Cinematic Arts, University of Southern California;

Soumith Chintala, Researcher, Meta; Kathryn Guarini, Chief Information Officer, IBM Corporation;

Paul Debevec, Chief Research Officer, Netflix Eyeline Studios;

Tonya Custis, Director of AI Research, Autodesk;

Toru Saito, Deputy Chief of Subaru Lab, Subaru Corporation;

Thomas Schulthess, Director, ETH Zurich/The Swiss National Supercomputing Center (CSCS);

Bill Vass, Vice President of Engineering, Amazon Web Services (AWS);

Chike Aguh, Chief Innovation Officer, US Department of Labor;

Tanya Simms, Director for Cyber Policy & Programs, Office of the National Cyber Director, Executive Office of the President;

Sergey Levine, Associate Professor, University of California, Berkeley;

TBA
2024Mar 18–21San JoseJensen Huang Blackwell architecture [46]

2021 notable speakers by industry sector

Research

Architecture, engineering, construction and design

Automotive

Topics around autonomous vehicles: techniques for developing safer, more efficient transportation, advancements in autonomous driving, end-to-end vehicle simulation, robotaxis, and trucking. [47]

Finance

Sessions concern impacts of technology advances in financial technology (Fintech). Presentations focus on how companies, consumers, and money interact across industries and how AI allows Fintech interactions to be personalized with recommendation engines, how self-service uses conversational AI, how transactions are secured with fraud-detection models. [48]

Healthcare

Sessions concern impacts of technology advances in healthcare.

Media, entertainment and gaming

Retail

Telecommunications

Sessions address subject matters concerning telecommunications and 5G: 5G network acceleration and security, AI-on-5G applications, and 6G research. [49]

2022 notable speakers and sessions

Conference topics

Accelerated computing and developer tools

Computer vision

Cybersecurity and Fraud detection

Data science

Data center

Deep learning

Internet of things (IoT), 5G, edge computing

Graphics

High performance computing (HPC)

Conversational AI and natural language processing

Recommenders and personalization

Autonomous machines

3D design collaboration and simulation

Video streaming and conferencing

XR (virtual and augmented reality)

Related Research Articles

<span class="mw-page-title-main">Nvidia</span> American multinational technology company

Nvidia Corporation is an American multinational corporation and technology company headquartered in Santa Clara, California, and incorporated in Delaware. It is a software and fabless company which designs and supplies graphics processing units (GPUs), application programming interfaces (APIs) for data science and high-performance computing, as well as system on a chip units (SoCs) for the mobile computing and automotive market. Nvidia is also a dominant supplier of artificial intelligence (AI) hardware and software.

<span class="mw-page-title-main">Graphics processing unit</span> Specialized electronic circuit; graphics accelerator

A graphics processing unit (GPU) is a specialized electronic circuit initially designed for digital image processing and to accelerate computer graphics, being present either as a discrete video card or embedded on motherboards, mobile phones, personal computers, workstations, and game consoles. After their initial design, GPUs were found to be useful for non-graphic calculations involving embarrassingly parallel problems due to their parallel structure. Other non-graphical uses include the training of neural networks and cryptocurrency mining.

General-purpose computing on graphics processing units is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing.

<span class="mw-page-title-main">Jensen Huang</span> American businessman (born 1963)

Jen-Hsun "Jensen" Huang is a Taiwanese-born American billionaire businessman, electrical engineer, and philanthropist who is the founder, president and chief executive officer (CEO) of Nvidia.

<span class="mw-page-title-main">CUDA</span> Parallel computing platform and programming model

In computing, CUDA is a proprietary parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs (GPGPU). CUDA API and its runtime: The CUDA API is an extension of the C programming language that adds the ability to specify thread-level parallelism in C and also to specify GPU device specific operations. CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.

<span class="mw-page-title-main">Tegra</span> System on a chip by Nvidia

Tegra is a system on a chip (SoC) series developed by Nvidia for mobile devices such as smartphones, personal digital assistants, and mobile Internet devices. The Tegra integrates an ARM architecture central processing unit (CPU), graphics processing unit (GPU), northbridge, southbridge, and memory controller onto one package. Early Tegra SoCs are designed as efficient multimedia processors. The Tegra-line evolved to emphasize performance for gaming and machine learning applications without sacrificing power efficiency, before taking a drastic shift in direction towards platforms that provide vehicular automation with the applied "Nvidia Drive" brand name on reference boards and its semiconductors; and with the "Nvidia Jetson" brand name for boards adequate for AI applications within e.g. robots or drones, and for various smart high level automation purposes.

<span class="mw-page-title-main">Nvidia Tesla</span> Nvidias line of general purpose GPUs

Nvidia Tesla is the former name for a line of products developed by Nvidia targeted at stream processing or general-purpose graphics processing units (GPGPU), named after pioneering electrical engineer Nikola Tesla. Its products began using GPUs from the G80 series, and have continued to accompany the release of new chips. They are programmable using the CUDA or OpenCL APIs.

<span class="mw-page-title-main">Volta (microarchitecture)</span> GPU microarchitecture by Nvidia

Volta is the codename, but not the trademark, for a GPU microarchitecture developed by Nvidia, succeeding Pascal. It was first announced on a roadmap in March 2013, although the first product was not announced until May 2017. The architecture is named after 18th–19th century Italian chemist and physicist Alessandro Volta. It was Nvidia's first chip to feature Tensor Cores, specially designed cores that have superior deep learning performance over regular CUDA cores. The architecture is produced with TSMC's 12 nm FinFET process. The Ampere microarchitecture is the successor to Volta.

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An AI accelerator, deep learning processor or neural processing unit (NPU) is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and computer vision. Typical applications include algorithms for robotics, Internet of Things, and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As of 2024, a typical AI integrated circuit chip contains tens of billions of MOSFETs.

<span class="mw-page-title-main">AlexNet</span> An influential convolutional neural network published in 2012

AlexNet is the name of a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto. It had 60 million parameters and 650,000 neurons.

Ampere is the codename for a graphics processing unit (GPU) microarchitecture developed by Nvidia as the successor to both the Volta and Turing architectures. It was officially announced on May 14, 2020 and is named after French mathematician and physicist André-Marie Ampère.

<span class="mw-page-title-main">Hopper (microarchitecture)</span> GPU microarchitecture designed by Nvidia

Hopper is a graphics processing unit (GPU) microarchitecture developed by Nvidia. It is designed for datacenters and is used alongside the Lovelace microarchitecture. It is the latest generation of the line of products formerly branded as Nvidia Tesla, now Nvidia Data Centre GPUs.

oneAPI (compute acceleration) Open standard for parallel computing

oneAPI is an open standard, adopted by Intel, for a unified application programming interface (API) intended to be used across different computing accelerator (coprocessor) architectures, including GPUs, AI accelerators and field-programmable gate arrays. It is intended to eliminate the need for developers to maintain separate code bases, multiple programming languages, tools, and workflows for each architecture.

Christofari — are Christofari (2019), Christofari Neo (2021) supercomputers of Sberbank based on Nvidia corporation hardware Sberbank of Russia and Nvidia. Their main purpose is neural network learning. They are also used for scientific research and commercial calculations.

Huang's law is an observation in computer science and engineering that advancements in graphics processing units (GPUs) are growing at a rate much faster than with traditional central processing units (CPUs). The observation is in contrast to Moore's law that predicted the number of transistors in a dense integrated circuit (IC) doubles about every two years. Huang's law states that the performance of GPUs will more than double every two years. The hypothesis is subject to questions about its validity.

Inspur Server Series is a series of server computers introduced in 1993 by Inspur, an information technology company, and later expanded to the international markets. The servers were likely among the first originally manufactured by a Chinese company. It is currently developed by Inspur Information and its San Francisco-based subsidiary company - Inspur Systems, both Inspur's spinoff companies. The product line includes GPU Servers, Rack-mounted servers, Open Computing Servers and Multi-node Servers.

PhyCV is the first computer vision library which utilizes algorithms directly derived from the equations of physics governing physical phenomena. The algorithms appearing in the first release emulate the propagation of light through a physical medium with natural and engineered diffractive properties followed by coherent detection. Unlike traditional algorithms that are a sequence of hand-crafted empirical rules, physics-inspired algorithms leverage physical laws of nature as blueprints. In addition, these algorithms can, in principle, be implemented in real physical devices for fast and efficient computation in the form of analog computing. Currently PhyCV has three algorithms, Phase-Stretch Transform (PST) and Phase-Stretch Adaptive Gradient-Field Extractor (PAGE), and Vision Enhancement via Virtual diffraction and coherent Detection (VEViD). All algorithms have CPU and GPU versions. PhyCV is now available on GitHub and can be installed from pip.

Ada Lovelace, also referred to simply as Lovelace, is a graphics processing unit (GPU) microarchitecture developed by Nvidia as the successor to the Ampere architecture, officially announced on September 20, 2022. It is named after the English mathematician Ada Lovelace, one of the first computer programmers. Nvidia announced the architecture along with the GeForce RTX 40 series consumer GPUs and the RTX 6000 Ada Generation workstation graphics card. The Lovelace architecture is fabricated on TSMC's custom 4N process which offers increased efficiency over the previous Samsung 8 nm and TSMC N7 processes used by Nvidia for its previous-generation Ampere architecture.

Blackwell is a graphics processing unit (GPU) microarchitecture developed by Nvidia as the successor to the Hopper and Ada Lovelace microarchitectures.

References

  1. ↑ "GTC 2021: #1 AI Conference".
  2. ↑ "NVIDIA Company History: Innovations over the Years".
  3. ↑ "GTC 2021 Conference Workshops & Training".
  4. ↑ "Jensen's Kitchen was a lie: Nvidia reveals GTC 2021 keynote nearly 100% fake". 12 August 2021.
  5. ↑ "Nvidia's 'Fermi' GPU architecture revealed". 30 September 2009.
  6. ↑ "Jen-Hsun Shows off NVIDIA GT300 'Fermi' Architecture".
  7. ↑ "Nvidia Unveils New Models of Quadro Graphics Cards". 4 October 2010.
  8. ↑ "World's Top Computational Scientists Coming to Beijing in December 2011 for NVIDIA GPU Technology Conference" (Press release). 30 August 2011.
  9. ↑ "NVIDIA Releases CUDA 4.1: CUDA Goes LLVM and Open Source (Kind Of)".
  10. ↑ "NVIDIA Opens up CUDA Compiler". 13 December 2011.
  11. ↑ "Celebrating the GPU: Seven Take-Aways from Nvidia's GTC 2012". 21 May 2012.
  12. ↑ "Nvidia announces GeForce Grid: Cloud gaming direct from a GPU, with games by Gaikai". 15 May 2012.
  13. ↑ "Goodbye Uncanny Valley: NVIDIA's 'Face Works' Brings Shocking Realism to Facial Animation". Forbes .
  14. ↑ Archived at Ghostarchive and the Wayback Machine : GPU Technology Conference 2014: Opening, NVLink (part 1) GTC. YouTube .
  15. ↑ "NVIDIA's next-generation GPU is called Pascal, and it's smaller, faster and more efficient". 25 March 2014.
  16. ↑ "Nvidia updates GeForce graphics, Tegra mobile roadmaps". ZDNet .
  17. ↑ Archived at Ghostarchive and the Wayback Machine : Opening Keynote at GTC 2015: Leaps in Visual Computing. YouTube .
  18. ↑ "NVIDIA GTC: The Race to Perfect Voice Recognition Using GPUs". Forbes .
  19. ↑ "NVIDIA Unleashes Monster Pascal GPU Card at GTC16". 6 April 2016.
  20. ↑ "Nvidia's monstrous Volta GPU appears, packed with 21 billion transistors and 5,120 cores".
  21. ↑ "GTC 2017 Keynote".
  22. ↑ "Nvidia Clara: World's fastest AI Inferences via GPU-based Architecture". 18 September 2018.
  23. ↑ "NVIDIA Partners with Arm to Bring Artificial Intelligence to Billions of Internet of Things Devices". April 2018.
  24. ↑ "GTC Europe 2018 Keynote - Part 1".
  25. ↑ "Stroke of Genius: NVIDIA Researchers Debut GauGAN at GTC 2019".
  26. ↑ "Nvidia Announces New Orin Auto AI Processor". Forbes .
  27. ↑ "NVIDIA Expands Driverless Vehicle Partnership with Toyota". 26 March 2019.
  28. ↑ "At GTC: Nvidia Expands Scope of Its AI and Datacenter Ecosystem". 20 March 2019.
  29. ↑ "NVIDIA's GPU Technology Conference goes online in October". 17 August 2020.
  30. ↑ "Nvidia Opens GTC with Plans for AI Supercomputer for Biomedical Research". 5 October 2020.
  31. ↑ "Nvidia intros new Ampere GPUs for visual computing". ZDNet .
  32. ↑ "Nvidia's 5 Biggest GTC 2020 Announcements: From A100 to SmartNICs". 14 May 2020.
  33. ↑ "Nvidia Asserts Itself as the AI Leader from the Edge to the Cloud". Forbes .
  34. ↑ "GPU Technology Conference 2019 Keynote".
  35. ↑ "Nvidia GPU Technology Conference Brings Out the AI All Stars". Forbes .
  36. ↑ "Nvidia unveils Grace ARM-based CPU for giant-scale AI and HPC apps". 12 April 2021.
  37. ↑ "Nvidia flexes some enterprise muscle at GTC 2021 conference". 20 April 2021.
  38. ↑ Knight, Will. "BMW's Virtual Factory Uses AI to Hone the Assembly Line". Wired.
  39. ↑ "Everything announced at Nvidia's GTC 2021: A data center CPU, SDK for quantum simulations and more". ZDNet .
  40. ↑ "Everything announced at Nvidia's GTC 2021: A data center CPU, SDK for quantum simulations and more". ZDNet .
  41. ↑ "Nvidia dips its toes into IaaS with subscriptions for DGX SuperPOD AI supercomputers".
  42. ↑ "Nvidia unveils BlueField 3 DPU. It's much faster". 12 April 2021.
  43. ↑ "NVIDIA CEO Jensen Huang Keynote at GTC 2022". NVIDIA. Retrieved 2022-04-30.
  44. ↑ "NVIDIA CEO Jensen Huang Keynote at GTC 2022". NVIDIA. Retrieved 2022-04-30.
  45. ↑ "NVIDIA CEO Jensen Huang Keynote at GTC 2022". NVIDIA. Retrieved 2022-04-30.
  46. ↑ Ray, Tiernan (March 19, 2024). "Nvidia CEO Jensen Huang unveils next-gen 'Blackwell' chip family at GTC". ZDNET.
  47. ↑ "Self-Driving Cars & Vehicles Conference Sessions at GTC21".
  48. ↑ "Fintech Conference Sessions at GTC21".
  49. ↑ "Telcommunications Conference Sessions at GTC21".
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