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17-627: NISC may refer to: No instruction set computing , an architecture designed for efficiency National Invitational Softball Championship , an American collegiate sports tournament National center of Incident readiness and Strategy for Cybersecurity , Japan's government institute for cybersecurity National Information Solutions Cooperative , information technology cooperative for utility and broadband companies See also [ edit ] All pages with titles beginning with NISC All pages with titles containing NISC Topics referred to by

34-525: A computer architecture based solely on pattern matching and absence of (micro-)instructions in the classical sense. These chips are known for being thought of as comparable to the neural networks , being marketed for the number of "synapses" and "neurons". The acronym ZISC alludes to reduced instruction set computer (RISC). ZISC is a hardware implementation of Kohonen networks (artificial neural networks) allowing massively parallel processing of very simple data (0 or 1). This hardware implementation

51-423: A compiler to translate high-level languages to RISC assembly code. Further advancement of compiler and memory technologies leads to emerging very long instruction word (VLIW) processors, where the compiler controls the schedule of instructions and handles data hazards. NISC is a successor of VLIW processors. In NISC, the compiler has both horizontal and vertical control of the operations in the datapath. Therefore,

68-411: A given datapath . Giving low-level control to the compiler enables better utilization of datapath resources, which ultimately result in better performance. The benefits of NISC technology are: The instruction set and controller of processors are the most tedious and time-consuming parts to design. By eliminating these two, design of custom processing elements become significantly easier. Furthermore,

85-833: A network can perform complex tasks. There are two main types of neural network. In the context of biology, a neural network is a population of biological neurons chemically connected to each other by synapses . A given neuron can be connected to hundreds of thousands of synapses. Each neuron sends and receives electrochemical signals called action potentials to its connected neighbors. A neuron can serve an excitatory role, amplifying and propagating signals it receives, or an inhibitory role, suppressing signals instead. Populations of interconnected neurons that are smaller than neural networks are called neural circuits . Very large interconnected networks are called large scale brain networks , and many of these together form brains and nervous systems . Signals generated by neural networks in

102-552: Is different from Wikidata All article disambiguation pages All disambiguation pages No instruction set computing NISC is a statically scheduled horizontal nanocoded architecture (SSHNA). The term "statically scheduled" means that the operation scheduling and Hazard handling are done by a compiler . The term "horizontal nanocoded" means that NISC does not have any predefined instruction set or microcode . The compiler generates nanocodes which directly control functional units , registers and multiplexers of

119-584: The EE Times , compared the NeuroMem chip with "The Machine", a machine capable of being able to predict crimes from scanning people's faces from the television series Person of Interest , describing it as "the heart of big data " and "foreshadow[ing] a real-life escalation in the era of massive data collection". In the past, microprocessor design technology evolved from complex instruction set computer (CISC) to reduced instruction set computer (RISC). In

136-505: The brain eventually travel through the nervous system and across neuromuscular junctions to muscle cells , where they cause contraction and thereby motion. In machine learning, a neural network is an artificial mathematical model used to approximate nonlinear functions. While early artificial neural networks were physical machines, today they are almost always implemented in software . Neurons in an artificial neural network are usually arranged into layers, with information passing from

153-548: The datapath of NISC processors can even be generated automatically for a given application. Therefore, designer's productivity is improved significantly. Since NISC datapaths are very efficient and can be generated automatically, NISC technology is comparable to high level synthesis (HLS) or C to HDL synthesis approaches. In fact, one of the benefits of this architecture style is its capability to bridge these two technologies (custom processor design and HLS). In computer science , zero instruction set computer ( ZISC ) refers to

170-529: The early days of the computer industry, compiler technology did not exist and programming was done in assembly language . To make programming easier, computer architects created complex instructions which were direct representations of high level functions of high level programming languages. Another force that encouraged instruction complexity was the lack of large memory blocks. As compiler and memory technologies advanced, RISC architectures were introduced. RISC architectures need more instruction memory and require

187-400: The first layer (the input layer) through one or more intermediate layers ( the hidden layers ) to the final layer (the output layer). The "signal" input to each neuron is a number, specifically a linear combination of the outputs of the connected neurons in the previous layer. The signal each neuron outputs is calculated from this number, according to its activation function . The behavior of

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204-434: The hardware is much simpler. However the control memory size is larger than the previous generations. To address this issue, low-overhead compression techniques can be used. Neural network A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models . While individual neurons are simple, many of them together in

221-572: The network depends on the strengths (or weights ) of the connections between neurons. A network is trained by modifying these weights through empirical risk minimization or backpropagation in order to fit some preexisting dataset. Neural networks are used to solve problems in artificial intelligence , and have thereby found applications in many disciplines, including predictive modeling , adaptive control , facial recognition , handwriting recognition , general game playing , and generative AI . The theoretical base for contemporary neural networks

238-423: The results of which are settled up disputing with each other. According to TechCrunch , software emulations of these types of chips are currently used for image recognition by many large tech companies, such as Facebook and Google . When applied to other miscellaneous pattern detection tasks, such as with text, results are said to be produced in microseconds even with chips released in 2007. Junko Yoshida, of

255-405: The same term [REDACTED] This disambiguation page lists articles associated with the title NISC . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=NISC&oldid=1182354075 " Category : Disambiguation pages Hidden categories: Short description

272-470: Was independently proposed by Alexander Bain in 1873 and William James in 1890. Both posited that human thought emerged from interactions among large numbers of neurons inside the brain. In 1949, Donald Hebb described Hebbian learning , the idea that neural networks can change and learn over time by strengthening a synapse every time a signal travels along it. Artificial neural networks were originally used to model biological neural networks starting in

289-583: Was invented by Guy Paillet and Pascal Tannhof (IBM), developed in cooperation with the IBM chip factory of Essonnes , in France, and was commercialized by IBM. The ZISC architecture alleviates the memory bottleneck by blending pattern memory with pattern learning and recognition logic. Their massively parallel computing solves the " winner takes all problem in action selection " by allotting each "neuron" its own memory and allowing simultaneous problem-solving

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