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Hardware For Artificial Intelligence
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. Lisp machines Lisp machines were developed in the late 1970s and early 1980s to make AI programs written in the programming language Lisp run faster. Neural network hardware Physical neural networks Dataflow architecture Dataflow architecture processors used for AI serve various purposes, with varied implementations like the polymorphic dataflow Convolution Engine by Kinara (formerly Deep Vision), structure-driven dataflow by Hailo, and dataflow scheduling by Cerebras. Component hardware AI accelerators 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. By 2019, graphics processing units (GPUs), often ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Memristor
A memristor (; a portmanteau of ''memory resistor'') is a non-linear two-terminal electrical component relating electric charge and magnetic flux linkage. It was described and named in 1971 by Leon Chua, completing a theoretical quartet of fundamental electrical components which comprises also the resistor, capacitor and inductor. Chua and Kang later generalized the concept to memristive systems. Such a system comprises a circuit, of multiple conventional components, which mimics key properties of the ideal memristor component and is also commonly referred to as a memristor. Several such memristor system technologies have been developed, notably ReRAM. The identification of memristive properties in electronic devices has attracted controversy. Experimentally, the ideal memristor has yet to be demonstrated. As a fundamental electrical component Chua in his 1971 paper identified a theoretical symmetry between the non-linear resistor (voltage vs. current), non-linear capacit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Hardware
Computer hardware includes the physical parts of a computer, such as the computer case, case, central processing unit (CPU), Random-access memory, random access memory (RAM), Computer monitor, monitor, Computer mouse, mouse, Computer keyboard, keyboard, computer data storage, graphics card, sound card, Computer speakers, speakers and motherboard. By contrast, software is the set of instructions that can be stored and run by hardware. Hardware is so-termed because it is "Hardness, hard" or rigid with respect to changes, whereas software is "soft" because it is easy to change. Hardware is typically directed by the software to execute any command or Instruction (computing), instruction. A combination of hardware and software forms a usable computing system, although Digital electronics, other systems exist with only hardware. Von Neumann architecture The template for all modern computers is the Von Neumann architecture, detailed in a First Draft of a Report on the EDVAC, 1945 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''Oxford English Dictionary'' of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lisp Machine
Lisp machines are general-purpose computers designed to efficiently run Lisp as their main software and programming language, usually via hardware support. They are an example of a high-level language computer architecture, and in a sense, they were the first commercial single-user workstations. Despite being modest in number (perhaps 7,000 units total as of 1988) Lisp machines commercially pioneered many now-commonplace technologies, including effective garbage collection, laser printing, windowing systems, computer mice, high-resolution bit-mapped raster graphics, computer graphic rendering, and networking innovations such as Chaosnet. Several firms built and sold Lisp machines in the 1980s: Symbolics (3600, 3640, XL1200, MacIvory, and other models), Lisp Machines Incorporated (LMI Lambda), Texas Instruments ( Explorer, MicroExplorer), and Xerox (Interlisp-D workstations). The operating systems were written in Lisp Machine Lisp, Interlisp (Xerox), and later partly in Common ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neuromorphic Engineering
Neuromorphic engineering, also known as neuromorphic computing, is the use of electronic circuits to mimic neuro-biological architectures present in the nervous system. A neuromorphic computer/chip is any device that uses physical artificial neurons (made from silicon) to do computations. In recent times, the term ''neuromorphic'' has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet. A key aspect of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Event Camera
An event camera, also known as a neuromorphic camera, silicon retina or dynamic vision sensor, is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional (frame) cameras do. Instead, each pixel inside an event camera operates independently and asynchronously, reporting changes in brightness as they occur, and staying silent otherwise. Functional description Event camera pixels independently respond to changes in brightness as they occur. Each pixel stores a reference brightness level, and continuously compares it to the current brightness level. If the difference in brightness exceeds a threshold, that pixel resets its reference level and generates an event: a discrete packet that contains the pixel address and timestamp. Events may also contain the polarity (increase or decrease) of a brightness change, or an instantaneous measurement of the illumination level. Thus, event cameras output an asynchrono ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Physical Neural Network
A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse. Types of physical neural networks ADALINE In the 1960s Bernard Widrow and Ted Hoff developed ADALINE (Adaptive Linear Neuron) which used electrochemical cells called memistors (memory resistors) to emulate synapses of an artificial neuron. The memistors were implemented as 3-terminal devices operating based on the reversible electroplating of copper such that the resistance between two of the terminals is controlled by the integral of the current applie ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lisp (programming Language)
Lisp (historically LISP) is a family of programming languages with a long history and a distinctive, fully parenthesized prefix notation. Originally specified in 1960, Lisp is the second-oldest high-level programming language still in common use, after Fortran. Lisp has changed since its early days, and many dialects have existed over its history. Today, the best-known general-purpose Lisp dialects are Common Lisp, Scheme, Racket and Clojure. Lisp was originally created as a practical mathematical notation for computer programs, influenced by (though not originally derived from) the notation of Alonzo Church's lambda calculus. It quickly became a favored programming language for artificial intelligence (AI) research. As one of the earliest programming languages, Lisp pioneered many ideas in computer science, including tree data structures, automatic storage management, dynamic typing, conditionals, higher-order functions, recursion, the self-hosting compiler, and the read†... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dataflow Architecture
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 is unpredictable, i.e., behavior is nondeterministic. Although no commercially successful general-purpose computer hardware has used a dataflow architecture, it has been successfully implemented in specialized hardware such as in digital signal processing, network routing, graphics processing, telemetry, and more recently in data warehousing, and artificial intelligence (as: polymorphic dataflow Convolution Engine, structure-driven, dataflow scheduling). It is also very relevant in many software architectures today including database engine designs and parallel computing frameworks. Synchr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Scheduling (computing)
In computing, scheduling is the action of assigning ''resources'' to perform ''tasks''. The ''resources'' may be processors, network links or expansion cards. The ''tasks'' may be threads, processes or data flows. The scheduling activity is carried out by a process called scheduler. Schedulers are often designed so as to keep all computer resources busy (as in load balancing), allow multiple users to share system resources effectively, or to achieve a target quality-of-service. Scheduling is fundamental to computation itself, and an intrinsic part of the execution model of a computer system; the concept of scheduling makes it possible to have computer multitasking with a single central processing unit (CPU). Goals A scheduler may aim at one or more goals, for example: * maximizing ''throughput'' (the total amount of work completed per time unit); * minimizing '' wait time'' (time from work becoming ready until the first point it begins execution); * minimizing '' latency ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cerebras
Cerebras Systems is an American artificial intelligence company with offices in Sunnyvale and San Diego, Toronto, Tokyo and Bangalore, India. Cerebras builds computer systems for complex artificial intelligence deep learning applications. History Cerebras was founded in 2015 by Andrew Feldman, Gary Lauterbach, Michael James, Sean Lie and Jean-Philippe Fricker. These five founders worked together at SeaMicro, which was started in 2007 by Feldman and Lauterbach and was later sold to AMD in 2012 for $334 million. In May 2016, Cerebras secured $27 million in series A funding led by Benchmark, Foundation Capital and Eclipse Ventures. In December 2016, series B funding was led by Coatue Management, followed in January 2017 with series C funding led by VY Capital. In November 2018, Cerebras closed its series D round with $88 million, making the company a unicorn. Investors in this round included Altimeter, VY Capital, Coatue, Foundation Capital, Benchmark, and Eclipse. On August ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Graphics Processing Unit
A graphics processing unit (GPU) is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mobile phones, personal computers, workstations, and game consoles. Modern GPUs are efficient at manipulating computer graphics and image processing. Their parallel structure makes them more efficient than general-purpose central processing units (CPUs) for algorithms that process large blocks of data in parallel. In a personal computer, a GPU can be present on a video card or embedded on the motherboard. In some CPUs, they are embedded on the CPU die. In the 1970s, the term "GPU" originally stood for ''graphics processor unit'' and described a programmable processing unit independently working from the CPU and responsible for graphics manipulation and output. Later, in 1994, Sony used the term (now standing for ''graphics processing unit'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
