Stream processingIn computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming, and distributed data processing. Stream processing systems aim to expose parallel processing for data streams and rely on streaming algorithms for efficient implementation.
Multiprocessor system on a chipA multiprocessor system on a chip ( (), ˌɛmˌpiː'sɒk or ˌɛmˌpiːˌɛsˌoʊˈsiː ) is a system on a chip (SoC) which includes multiple microprocessors. As such, it is a multi-core system on a chip. MPSoCs are usually targeted for embedded applications. It is used by platforms that contain multiple, usually heterogeneous, processing elements with specific functionalities reflecting the need of the expected application domain, a memory hierarchy and I/O components.
Data processing unitA data processing unit (DPU) is a programmable computer processor that tightly integrates a general-purpose CPU with network interface hardware. Sometimes they are called "IPUs" (for "infrastructure processing unit") or "SmartNICs". They can be used in place of traditional NICs to relieve the main CPU of complex networking responsibilities and other "infrastructural" duties; although their features vary, they may be used to perform encryption/decryption, serve as a firewall, handle TCP/IP, process HTTP requests, or even function as a hypervisor or storage controller.
Massively parallelMassively parallel is the term for using a large number of computer processors (or separate computers) to simultaneously perform a set of coordinated computations in parallel. GPUs are massively parallel architecture with tens of thousands of threads. One approach is grid computing, where the processing power of many computers in distributed, diverse administrative domains is opportunistically used whenever a computer is available. An example is BOINC, a volunteer-based, opportunistic grid system, whereby the grid provides power only on a best effort basis.
C to HDLC to HDL tools convert C language or C-like computer code into a hardware description language (HDL) such as VHDL or Verilog. The converted code can then be synthesized and translated into a hardware device such as a field-programmable gate array. Compared to software, equivalent designs in hardware consume less power (yielding higher performance per watt) and execute faster with lower latency, more parallelism and higher throughput.
Flow to HDLFlow to HDL tools and methods convert flow-based system design into a hardware description language (HDL) such as VHDL or Verilog. Typically this is a method of creating designs for field-programmable gate array, application-specific integrated circuit prototyping and digital signal processing (DSP) design. Flow-based system design is well-suited to field-programmable gate array design as it is easier to specify the innate parallelism of the architecture. The use of flow-based design tools in engineering is a reasonably new trend.
Massively parallel processor arrayA massively parallel processor array, also known as a multi purpose processor array (MPPA) is a type of integrated circuit which has a massively parallel array of hundreds or thousands of CPUs and RAM memories. These processors pass work to one another through a reconfigurable interconnect of channels. By harnessing a large number of processors working in parallel, an MPPA chip can accomplish more demanding tasks than conventional chips. MPPAs are based on a software parallel programming model for developing high-performance embedded system applications.
Tensor Processing UnitTensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. Google began using TPUs internally in 2015, and in 2018 made them available for third party use, both as part of its cloud infrastructure and by offering a smaller version of the chip for sale. Compared to a graphics processing unit, TPUs are designed for a high volume of low precision computation (e.g.
Heterogeneous computingHeterogeneous computing refers to systems that use more than one kind of processor or core. These systems gain performance or energy efficiency not just by adding the same type of processors, but by adding dissimilar coprocessors, usually incorporating specialized processing capabilities to handle particular tasks. Usually heterogeneity in the context of computing referred to different instruction-set architectures (ISA), where the main processor has one and other processors have another - usually a very different - architecture (maybe more than one), not just a different microarchitecture (floating point number processing is a special case of this - not usually referred to as heterogeneous).
Performance per wattIn computing, performance per watt is a measure of the energy efficiency of a particular computer architecture or computer hardware. Literally, it measures the rate of computation that can be delivered by a computer for every watt of power consumed. This rate is typically measured by performance on the LINPACK benchmark when trying to compare between computing systems: an example using this is the Green500 list of supercomputers. Performance per watt has been suggested to be a more sustainable measure of computing than Moore’s Law.
