Memory addressIn computing, a memory address is a reference to a specific memory location used at various levels by software and hardware. Memory addresses are fixed-length sequences of digits conventionally displayed and manipulated as unsigned integers. Such numerical semantic bases itself upon features of CPU (such as the instruction pointer and incremental address registers), as well upon use of the memory like an array endorsed by various programming languages. A digital computer's main memory consists of many memory locations.
Flash memoryFlash memory is an electronic non-volatile computer memory storage medium that can be electrically erased and reprogrammed. The two main types of flash memory, NOR flash and NAND flash, are named for the NOR and NAND logic gates. Both use the same cell design, consisting of floating gate MOSFETs. They differ at the circuit level depending on whether the state of the bit line or word lines is pulled high or low: in NAND flash, the relationship between the bit line and the word lines resembles a NAND gate; in NOR flash, it resembles a NOR gate.
CPU cacheA CPU cache is a hardware cache used by the central processing unit (CPU) of a computer to reduce the average cost (time or energy) to access data from the main memory. A cache is a smaller, faster memory, located closer to a processor core, which stores copies of the data from frequently used main memory locations. Most CPUs have a hierarchy of multiple cache levels (L1, L2, often L3, and rarely even L4), with different instruction-specific and data-specific caches at level 1.
Chip carrierIn electronics, a chip carrier is one of several kinds of surface-mount technology packages for integrated circuits (commonly called "chips"). Connections are made on all four edges of a square package; compared to the internal cavity for mounting the integrated circuit, the package overall size is large. Chip carriers may have either J-shaped metal leads for connections by solder or by a socket, or may be lead-less with metal pads for connections. If the leads extend beyond the package, the preferred description is "flat pack".
In-memory processingIn computer science, in-memory processing (PIM) is a computer architecture for processing data stored in an in-memory database. In-memory processing improves the power usage and performance of moving data between the processor and the main memory. Older systems have been based on disk storage and relational databases using Structured Query Language, which are increasingly regarded as inadequate to meet business intelligence (BI) needs.
Parallel programming modelIn computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in programs. The value of a programming model can be judged on its generality: how well a range of different problems can be expressed for a variety of different architectures, and its performance: how efficiently the compiled programs can execute. The implementation of a parallel programming model can take the form of a library invoked from a sequential language, as an extension to an existing language, or as an entirely new language.
PIC microcontrollersPIC (usually pronounced as [phɪk]) is a family of microcontrollers made by Microchip Technology, derived from the PIC1650 originally developed by General Instrument's Microelectronics Division. The name PIC initially referred to Peripheral Interface Controller, and is currently expanded as Programmable Intelligent Computer. The first parts of the family were available in 1976; by 2013 the company had shipped more than twelve billion individual parts, used in a wide variety of embedded systems.
Granularity (parallel computing)In parallel computing, granularity (or grain size) of a task is a measure of the amount of work (or computation) which is performed by that task. Another definition of granularity takes into account the communication overhead between multiple processors or processing elements. It defines granularity as the ratio of computation time to communication time, wherein computation time is the time required to perform the computation of a task and communication time is the time required to exchange data between processors.
Cache (computing)In computing, a cache (kæʃ ) is a hardware or software component that stores data so that future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation or a copy of data stored elsewhere. A cache hit occurs when the requested data can be found in a cache, while a cache miss occurs when it cannot. Cache hits are served by reading data from the cache, which is faster than recomputing a result or reading from a slower data store; thus, the more requests that can be served from the cache, the faster the system performs.
Autonomous peripheral operationIn computing, autonomous peripheral operation is a hardware feature found in some microcontroller architectures to off-load certain tasks into embedded autonomous peripherals in order to minimize latencies and improve throughput in hard real-time applications as well as to save energy in ultra-low-power designs. Forms of autonomous peripherals in microcontrollers were first introduced in the 1990s.
History of computing hardwareThe history of computing hardware covers the developments from early simple devices to aid calculation to modern day computers. The first aids to computation were purely mechanical devices which required the operator to set up the initial values of an elementary arithmetic operation, then manipulate the device to obtain the result. Later, computers represented numbers in a continuous form (e.g. distance along a scale, rotation of a shaft, or a voltage). Numbers could also be represented in the form of digits, automatically manipulated by a mechanism.
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.
