Integrated circuit layout design protectionLayout designs (topographies) of integrated circuits are a field in the protection of intellectual property. In United States intellectual property law, a "mask work" is a two or three-dimensional layout or topography of an integrated circuit (IC or "chip"), i.e. the arrangement on a chip of semiconductor devices such as transistors and passive electronic components such as resistors and interconnections.
Intel Core (microarchitecture)The Intel Core microarchitecture (provisionally referred to as Next Generation Micro-architecture, and developed as Merom) is a multi-core processor microarchitecture launched by Intel in mid-2006. It is a major evolution over the Yonah, the previous iteration of the P6 microarchitecture series which started in 1995 with Pentium Pro. It also replaced the NetBurst microarchitecture, which suffered from high power consumption and heat intensity due to an inefficient pipeline designed for high clock rate.
Q-learningQ-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.
Cyrix 6x86The Cyrix 6x86 is a line of sixth-generation, 32-bit x86 microprocessors designed and released by Cyrix in 1995. Cyrix, being a fabless company, had the chips manufactured by IBM and SGS-Thomson. The 6x86 was made as a direct competitor to Intel's Pentium microprocessor line, and was pin compatible. During the 6x86's development, the majority of applications (office software as well as games) performed almost entirely integer operations. The designers foresaw that future applications would most likely maintain this instruction focus.
Floating-point unitA floating-point unit (FPU, colloquially a math coprocessor) is a part of a computer system specially designed to carry out operations on floating-point numbers. Typical operations are addition, subtraction, multiplication, division, and square root. Some FPUs can also perform various transcendental functions such as exponential or trigonometric calculations, but the accuracy can be very low, so that some systems prefer to compute these functions in software.
Transistor–transistor logicTransistor–transistor logic (TTL) is a logic family built from bipolar junction transistors. Its name signifies that transistors perform both the logic function (the first "transistor") and the amplifying function (the second "transistor"), as opposed to earlier resistor–transistor logic (RTL) and diode–transistor logic (DTL). TTL integrated circuits (ICs) were widely used in applications such as computers, industrial controls, test equipment and instrumentation, consumer electronics, and synthesizers.
Superscalar processorA superscalar processor is a CPU that implements a form of parallelism called instruction-level parallelism within a single processor. In contrast to a scalar processor, which can execute at most one single instruction per clock cycle, a superscalar processor can execute more than one instruction during a clock cycle by simultaneously dispatching multiple instructions to different execution units on the processor. It therefore allows more throughput (the number of instructions that can be executed in a unit of time) than would otherwise be possible at a given clock rate.
Double-precision floating-point formatDouble-precision floating-point format (sometimes called FP64 or float64) is a floating-point number format, usually occupying 64 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point. Floating point is used to represent fractional values, or when a wider range is needed than is provided by fixed point (of the same bit width), even if at the cost of precision. Double precision may be chosen when the range or precision of single precision would be insufficient.
Linear integrated circuitA linear integrated circuit or analog chip is a set of miniature electronic analog circuits formed on a single piece of semiconductor material. The voltage and current at specified points in the circuits of analog chips vary continuously over time. In contrast, digital chips only assign meaning to voltages or currents at discrete levels. In addition to transistors, analog chips often include a larger number of passive elements (capacitors, resistors, and inductors) than digital chips.
False precisionFalse precision (also called overprecision, fake precision, misplaced precision and spurious precision) occurs when numerical data are presented in a manner that implies better precision than is justified; since precision is a limit to accuracy (in the ISO definition of accuracy), this often leads to overconfidence in the accuracy, named precision bias. Madsen Pirie defines the term "false precision" in a more general way: when exact numbers are used for notions that cannot be expressed in exact terms.
XeonXeon (ˈziːɒn ) is a brand of x86 microprocessors designed, manufactured, and marketed by Intel, targeted at the non-consumer workstation, server, and embedded system markets. It was introduced in June 1998. Xeon processors are based on the same architecture as regular desktop-grade CPUs, but have advanced features such as support for ECC memory, higher core counts, more PCI Express lanes, support for larger amounts of RAM, larger cache memory and extra provision for enterprise-grade reliability, availability and serviceability (RAS) features responsible for handling hardware exceptions through the Machine Check Architecture.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. 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.
