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Nvidia Corporation is an American multinational technology company incorporated in Delaware and based in Santa Clara, California. It is a software and fabless company which designs graphics processing units (GPUs), application programming interface (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 a dominant supplier of artificial intelligence hardware and software. Its professional line of GPUs are used in workstations for applications in such fields as architecture, engineering and construction, media and entertainment, automotive, scientific research, and manufacturing design.
A coprocessor is a computer processor used to supplement the functions of the primary processor. Operations performed by the coprocessor may be floating-point arithmetic, graphics, signal processing, string processing, cryptography or I/O interfacing with peripheral devices. By offloading processor-intensive tasks from the main processor, coprocessors can accelerate system performance. Coprocessors allow a line of computers to be customized, so that customers who do not need the extra performance do not need to pay for it.
A graphics processing unit (GPU) is a specialized electronic circuit initially designed to accelerate computer graphics and image processing, but have later been used 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.
A physics processing unit (PPU) is a dedicated microprocessor designed to handle the calculations of physics, especially in the physics engine of video games. It is an example of hardware acceleration.
CUDA is a proprietary and closed source parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for general purpose processing, an approach called general-purpose computing on GPUs (GPGPU). 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.
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.
Arm is a British semiconductor and software design company based in Cambridge, England whose primary business is the design of ARM processors (CPUs). It also designs other chips, provides software development tools under the DS-5, RealView and Keil brands, and provides systems and platforms, system-on-a-chip (SoC) infrastructure and software. As a "holding" company, it also holds shares of other companies. Since 2016, it has been owned by Japanese conglomerate SoftBank Group.
Project Denver is the codename of a central processing unit designed by Nvidia that implements the ARMv8-A 64/32-bit instruction sets using a combination of simple hardware decoder and software-based binary translation where "Denver's binary translation layer runs in software, at a lower level than the operating system, and stores commonly accessed, already optimized code sequences in a 128 MB cache stored in main memory". Denver is a very wide in-order superscalar pipeline. Its design makes it suitable for integration with other SIPs cores into one die constituting a system on a chip (SoC).
Nvidia Tesla was the name of Nvidia's line of products 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.
Heterogeneous computing refers to systems that use more than one kind of processor or core. These systems gain performance or energy efficiency not just by adding the same type of processors, but by adding dissimilar coprocessors, usually incorporating specialized processing capabilities to handle particular tasks.
Volta is the codename 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.
Nvidia Drive is a computer platform by Nvidia, aimed at providing autonomous car and driver assistance functionality powered by deep learning. The platform was introduced at the Consumer Electronics Show (CES) in Las Vegas in January 2015. An enhanced version, the Drive PX 2 was introduced at CES a year later, in January 2016.
A vision processing unit (VPU) is an emerging class of microprocessor; it is a specific type of AI accelerator, designed to accelerate machine vision tasks.
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. Google began using TPUs internally in 2015, and in 2018 made them available for third party use, both as part of its cloud infrastructure and by offering a smaller version of the chip for sale.
An AI accelerator is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and machine 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 2018, a typical AI integrated circuit chip contains billions of MOSFET transistors. A number of vendor-specific terms exist for devices in this category, and it is an emerging technology without a dominant design.
Nvidia DGX is a line of Nvidia-produced servers and workstations which specialize in using GPGPU to accelerate deep learning applications. The typical design of a DGX system is based upon a rackmount chassis with motherboard that carries high performance x86 server CPUs. The main component of a DGX system is a set of 4 to 16 Nvidia Tesla GPU modules on an independent system board. DGX systems have large heatsinks and powerful fans to adequately cool thousands of watts of thermal output. The GPU modules are typically integrated into the system using a version of the SXM socket.
Nvidia Jetson is a series of embedded computing boards from Nvidia. The Jetson TK1, TX1 and TX2 models all carry a Tegra processor from Nvidia that integrates an ARM architecture central processing unit (CPU). Jetson is a low-power system and is designed for accelerating machine learning applications.
ROCm is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing. It offers several programming models: HIP, OpenMP/Message Passing Interface (MPI), OpenCL.
NVIDIA GTC is a global AI conference for developers that brings together developers, engineers, researchers, inventors, and IT professionals. Topics focus on artificial intelligence (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.
Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. As of 2023, the market for AI hardware is dominated by GPUs.
References
- ↑ Freund, Karl (March 29, 2018). "Arm Chooses NVIDIA Open-Source CNN AI Chip Technology". Forbes. Moor Insights and Strategy. Retrieved June 13, 2018.
- ↑ "NVDLA Primer — NVDLA Documentation". nvdla.org.
- ↑ Kwon, Hyoukjun; Pellauer, Michael; Krishna, Tushar (2018). "MAESTRO: An Open-source Infrastructure for Modeling Dataflows within Deep Learning Accelerators". arXiv: 1805.02566 [cs.DC].
- 1 2 Franklin, Dustin (6 November 2019). "Introducing Jetson Xavier NX, the World's Smallest AI Supercomputer". developer.nvidia.com. Nvidia . Retrieved 23 September 2020.
- ↑ Wong, William G. (5 December 2017). "The Rise of RISC-V on Display at Workshop". electronicdesign.com. Endeavor Business Media. Retrieved 23 September 2020.
Nvidia gave a presentation about how its proprietary Falcon (Fast Logic CONtroller) core will be replaced by RISC-V cores. Falcon is a RISC CPU used in a number of Nvidia's chips, such as the Jetson TX2.