Reconfigurable computing

Reconfigurable computing is a computer architecture combining some of the flexibility of software with the high performance of hardware by processing with very flexible high speed computing fabrics like field-programmable gate arrays (FPGAs). The principal difference when compared to using ordinary microprocessors is the ability to make substantial changes to the datapath itself in addition to the control flow. On the other hand, the main difference from custom hardware, i.e. application-specific integrated circuits (ASICs) is the possibility to adapt the hardware during runtime by 'loading' a new circuit on the reconfigurable fabric. The concept of reconfigurable computing has existed since the 1960s, when Gerald Estrin's paper proposed the concept of a computer made of a standard processor and an array of 'reconfigurable' hardware. The main processor would control the behavior of the reconfigurable hardware. The latter would then be tailored to perform a specific task, such as image processing or pattern matching, as quickly as a dedicated piece of hardware. Once the task was done, the hardware could be adjusted to do some other task. This resulted in a hybrid computer structure combining the flexibility of software with the speed of hardware. In the 1980s and 1990s there was a renaissance in this area of research with many proposed reconfigurable architectures developed in industry and academia, such as: Copacobana, Matrix, GARP, Elixent, NGEN, Polyp, MereGen, PACT XPP, Silicon Hive, Montium, Pleiades, Morphosys, and PiCoGA. Such designs were feasible due to the constant progress of silicon technology that let complex designs be implemented on one chip. Some of these massively parallel reconfigurable computers were built primarily for special subdomains such as molecular evolution, neural or image processing. The world's first commercial reconfigurable computer, the Algotronix CHS2X4, was completed in 1991. It was not a commercial success, but was promising enough that Xilinx (the inventor of the Field-Programmable Gate Array, FPGA) bought the technology and hired the Algotronix staff. Later machines enabled first demonstrations of scientific principles, such as the spontaneous spatial self-organisation of genetic coding with MereGen. The fundamental model of the reconfigurable computing machine paradigm, the data-stream-based anti machine is well illustrated by the differences to other machine paradigms that were introduced earlier, as shown by Nick Tredennick's following classification scheme of computing paradigms (see 'Table 1: Nick Tredennick’s Paradigm Classification Scheme'). Computer scientist Reiner Hartenstein describes reconfigurable computing in terms of an anti-machine that, according to him, represents a fundamental paradigm shift away from the more conventional von Neumann machine. Hartenstein calls it Reconfigurable Computing Paradox, that software-to-configware (software-to-FPGA) migration results in reported speed-up factors of up to more than four orders of magnitude, as well as a reduction in electricity consumption by up to almost four orders of magnitude—although the technological parameters of FPGAs are behind the Gordon Moore curve by about four orders of magnitude, and the clock frequency is substantially lower than that of microprocessors. This paradox is partly explained by the Von Neumann syndrome. High-Performance Reconfigurable Computing (HPRC) is a computer architecture combining reconfigurable computing-based accelerators like field-programmable gate array with CPUs or multi-core processors. The increase of logic in an FPGA has enabled larger and more complex algorithms to be programmed into the FPGA. The attachment of such an FPGA to a modern CPU over a high speed bus, like PCI express, has enabled the configurable logic to act more like a coprocessor rather than a peripheral. This has brought reconfigurable computing into the high-performance computing sphere. Furthermore, by replicating an algorithm on an FPGA or the use of a multiplicity of FPGAs has enabled reconfigurable SIMD systems to be produced where several computational devices can concurrently operate on different data, which is highly parallel computing.