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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. Since 2017, several consumer grade CPUs and SoCs have on-die NPUs. As of 2023, the market for AI hardware is dominated by GPUs. [1]
Lisp machines were developed in the late 1970s and early 1980s to make Artificial intelligence programs written in the programming language Lisp run faster.
Dataflow architecture processors used for AI serve various purposes, with varied implementations like the polymorphic dataflow [2] Convolution Engine [3] by Kinara (formerly Deep Vision), structure-driven dataflow by Hailo, [4] and dataflow scheduling by Cerebras. [5]
Since the 2010s, advances in computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer. [6] By 2019, graphics processing units (GPUs), often with AI-specific enhancements, had displaced central processing units (CPUs) as the dominant means to train large-scale commercial cloud AI. [7] OpenAI estimated the hardware compute used in the largest deep learning projects from Alex Net (2012) to Alpha Zero (2017), and found a 300,000-fold increase in the amount of compute needed, with a doubling-time trend of 3.4 months. [8] [9]
Dataflow architecture is a dataflow-based computer architecture that directly contrasts the traditional von Neumann architecture or control flow architecture. Dataflow architectures have no program counter, in concept: the executability and execution of instructions is solely determined based on the availability of input arguments to the instructions, so that the order of instruction execution may be hard to predict.
Hardware acceleration is the use of computer hardware designed to perform specific functions more efficiently when compared to software running on a general-purpose central processing unit (CPU). Any transformation of data that can be calculated in software running on a generic CPU can also be calculated in custom-made hardware, or in some mix of both.
Processor may refer to:
Manycore processors are special kinds of multi-core processors designed for a high degree of parallel processing, containing numerous simpler, independent processor cores. Manycore processors are used extensively in embedded computers and high-performance computing.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
In computing and computer science, a processor or processing unit is an electrical component that performs operations on an external data source, usually memory or some other data stream. It typically takes the form of a microprocessor, which can be implemented on a single or a few tightly integrated metal–oxide–semiconductor integrated circuit chips. In the past, processors were constructed using multiple individual vacuum tubes, multiple individual transistors, or multiple integrated circuits.
A cognitive computer is a computer that hardwires artificial intelligence and machine learning algorithms into an integrated circuit that closely reproduces the behavior of the human brain. It generally adopts a neuromorphic engineering approach. Synonyms include neuromorphic chip and cognitive chip.
A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. Convolution-based networks are the de-facto standard in deep learning-based approaches to computer vision and image processing, and have only recently been replaced -- in some cases -- by newer deep learning architectures such as the transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer, 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution kernels, only 25 neurons are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features.
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.
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.
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.
AMD Instinct is AMD's brand of data center GPUs. It replaced AMD's FirePro S brand in 2016. Compared to the Radeon brand of mainstream consumer/gamer products, the Instinct product line is intended to accelerate deep learning, artificial neural network, and high-performance computing/GPGPU applications.
AlexNet is 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 in 2012. It had 60 million parameters and 650,000 neurons.
PyTorch is a machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella. It is one of the most popular deep learning frameworks, alongside others such as TensorFlow and PaddlePaddle, offering free and open-source software released under the modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface.
The NVIDIA Deep Learning Accelerator (NVDLA) is an open-source hardware neural network AI accelerator created by Nvidia. The accelerator is written in Verilog and is configurable and scalable to meet many different architecture needs. NVDLA is merely an accelerator and any process must be scheduled and arbitered by an outside entity such as a CPU.
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.
Huang's law is the 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.
Cerebras Systems Inc. is an American artificial intelligence (AI) company with offices in Sunnyvale, San Diego, Toronto, and Bangalore, India. Cerebras builds computer systems for complex AI deep learning applications.
Kinara is an American semiconductor company that develops AI processors for machine learning applications.
