Optimizing compilerIn computing, an optimizing compiler is a compiler that tries to minimize or maximize some attributes of an executable computer program. Common requirements are to minimize a program's execution time, memory footprint, storage size, and power consumption (the last three being popular for portable computers). Compiler optimization is generally implemented using a sequence of optimizing transformations, algorithms which take a program and transform it to produce a semantically equivalent output program that uses fewer resources or executes faster.
Silicon–germaniumSiGe (ˈsɪɡiː or ˈsaɪdʒiː), or silicon–germanium, is an alloy with any molar ratio of silicon and germanium, i.e. with a molecular formula of the form Si1−xGex. It is commonly used as a semiconductor material in integrated circuits (ICs) for heterojunction bipolar transistors or as a strain-inducing layer for CMOS transistors. IBM introduced the technology into mainstream manufacturing in 1989. This relatively new technology offers opportunities in mixed-signal circuit and analog circuit IC design and manufacture.
Tool use by non-humansTool use by non-humans is a phenomenon in which a non-human animal uses any kind of tool in order to achieve a goal such as acquiring food and water, grooming, combat, defence, communication, recreation or construction. Originally thought to be a skill possessed only by humans, some tool use requires a sophisticated level of cognition. There is considerable discussion about the definition of what constitutes a tool and therefore which behaviours can be considered true examples of tool use.
Estimation theoryEstimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An estimator attempts to approximate the unknown parameters using the measurements.
Polycrystalline siliconPolycrystalline silicon, or multicrystalline silicon, also called polysilicon, poly-Si, or mc-Si, is a high purity, polycrystalline form of silicon, used as a raw material by the solar photovoltaic and electronics industry. Polysilicon is produced from metallurgical grade silicon by a chemical purification process, called the Siemens process. This process involves distillation of volatile silicon compounds, and their decomposition into silicon at high temperatures. An emerging, alternative process of refinement uses a fluidized bed reactor.
SiliconSilicon is a chemical element with the symbol Si and atomic number 14. It is a hard, brittle crystalline solid with a blue-grey metallic luster, and is a tetravalent metalloid and semiconductor. It is a member of group 14 in the periodic table: carbon is above it; and germanium, tin, lead, and flerovium are below it. It is relatively unreactive. Because of its high chemical affinity for oxygen, it was not until 1823 that Jöns Jakob Berzelius was first able to prepare it and characterize it in pure form.
Switched-mode power supplyA switched-mode power supply (switching-mode power supply, switch-mode power supply, switched power supply, SMPS, or switcher) is an electronic power supply that incorporates a switching regulator to convert electrical power efficiently. Like other power supplies, an SMPS transfers power from a DC or AC source (often mains power, see AC adapter) to DC loads, such as a personal computer, while converting voltage and current characteristics.
Silicon on sapphireSilicon on sapphire (SOS) is a hetero-epitaxial process for metal–oxide–semiconductor (MOS) integrated circuit (IC) manufacturing that consists of a thin layer (typically thinner than 0.6 μm) of silicon grown on a sapphire (Al2O3) wafer. SOS is part of the silicon-on-insulator (SOI) family of CMOS (complementary MOS) technologies. Typically, high-purity artificially grown sapphire crystals are used. The silicon is usually deposited by the decomposition of silane gas (SiH4) on heated sapphire substrates.
Training, validation, and test data setsIn machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
