Generic Address Generator

Reconfigurable computing

Reconfigurable computing is a computing paradigm combining some of the flexibility of software with the high performance of hardware by processing with very flexible high speed computing fabrics like 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 with custom hardware (ASICs) is the possibility to adapt the hardware during runtime by "loading" a new circuit on the reconfigurable fabric.

History and properties

The concept of reconfigurable computing has existed since the 1960s, when Gerald Estrin's landmark 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; unfortunately this idea was far ahead of its time in needed electronic technology.

In the eighties and nineties, there was a renaissance in this area of research with many proposed reconfigurable architectures developed in industry and academia, such as: Matrix, Garp, Elixent, PACT XPP, Silicon Hive, Montium, Pleiades, Morphosys, PiCoGA. Such designs were feasible due to the constant progress of silicon technology that let complex designs be implemented on one chip. 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.

Current systems

Currently there are a number of vendors with commercially available reconfigurable computers aimed at the high performance computing market; including Cray, SGI and SRC Computers, Inc. . The reconfigurable computers are "Estrin" hybrid computers with microprocessors that can be used in traditional CPU cluster computers or coupled to user-programmable FPGAs for hybrid computing. Cray supercomputer company (not affiliated with SRC Computers) acquired OctigaBay and its reconfigurable computing platform, which Cray marketed as the XD1 until recently. SGI sells the RASC platform with their Altix series of supercomputers. SRC Computers, Inc. has developed a family of reconfigurable computers based on their IMPLICIT+EXPLICIT architecture and MAP processor.

The XD1 and SGI FPGA reconfiguration can be accomplished either via the traditional Hardware Description Languages (HDL), which can be generated directly or by using electronic design automation (“EDA”) or electronic system level (“ESL”) tools, employing high level languages like the graphical tool Starbridge Viva or C-based languages like for example Handel-C from Celoxica, DIME-C from Nallatech, Impulse C from Impulse Accelerated Technologies or Mitrion-C from Mitrionics.

In addition, Mitrionics has developed a Software Acceleration Platform that enables software written using a single assignment language to be compiled and executed on FPGA-based computers, such as those from Cray and SGI. The Mitrion-C software language and Mitrion Virtual Processor enable software developers to write and execute applications on FPGA-based computers in the same manner as with other computing technologies, such as graphical processing units (“GPUs”), cell-based processors, parallel processing units (“PPUs”), multi-core CPUs, and traditional single-core CPU clusters. SRC has developed a "Carte" compiler that takes an existing high-level languages like C or Fortran, and with a few modifications, compiles them for execution on both the FPGA and microprocessor. According to SRC literature, "...application algorithms are written in a high-level language such as C or Fortran. Carte extracts the maximum parallelism from the code and generates pipelined hardware logic that is instantiated in the MAP. It also generates all the required interface code to manage the movement of data to and from the MAP and to coordinate the microprocessor with the logic running in the MAP." (note that SRC also allows a traditional HDL flow to be used). The SRC systems communicate via the SNAP memory interface, and/or the (optional) Hi-Bar switch.

The Research community is also acting on the subject with projects like MORPHEUS in Europe which implements on a single 100mm² 90nm chip an ARM9 processor, a M2000 FPGA, a DREAM picoGA and a PACT XPP matrix.

Comparison of systems

As an emerging field, classifications of reconfigurable architectures are still being developed and refined as new architectures are developed; no unifying taxonomy has been suggested to date. However, several recurring parameters can be used to classify these systems.


The granularity of the reconfigurable logic is defined as the size of the smallest functional unit (CLB) that is addressed by the mapping tools. Low granularity, which can also be known as fine-grained, often implies a greater flexibility when implementing algorithms into the hardware. However, there is a penalty associated with this in terms of increased power, area and delay due to greater quantity of routing required per computation. Fine-grained architectures work at the bit-level manipulation level; whilst coarse grained processing elements (rDPU) are better optimised for standard data path applications. One of the drawbacks of coarse grained architectures are that they tend to lose some of their utilisation and performance if they need to perform smaller computations than their granularity provides, for example for a one bit add on a four bit wide functional unit would waste three bits. This problem can be solved by having a coarse grain array (rDPA) and a FPGA on the same chip.

Coarse-grained architectures (rDPA) are intended for the implementation for algorithms needing word-width data paths (rDPU). As their functional blocks are optimized for large computations and typically comprise word wide ALUs. They will perform these operations more quickly and power efficiently than a smaller set of functional units connected together with some interconnect, this is due to the connecting wires are shorter, meaning less wire capacitance and hence faster and lower power designs. A potential undesirable consequence of having larger computational blocks is that when the size of operands may not match the algorithm an inefficient utilisation of resources can result. Often the type of applications to be run are known in advance allowing the logic, memory and routing resources to be tailored (for instance, see KressArray Xplorer) to enhance the performance of the device whilst still providing a certain level of flexibility for future adaptation. Examples of this are domain specific arrays aimed at gaining better performance in terms of power, area, throughput than their more generic finer grained FPGA cousins by reducing their flexibility.

Rate of reconfiguration

Configuration of these reconfigurable systems can happen at deployment time, between execution phases or during execution. In a typical reconfigurable system, a bit stream is used to program the device at deployment time. Fine grained systems by their own nature requires greater configuration time than more coarse-grained architectures due to more elements needing to be addressed and programmed. Therefore more coarse-grained architectures gain from potential lower energy requirements, as less information is transferred and utilised. Intuitively, the slower the rate of reconfiguration the smaller the energy consumption as the associated energy cost of reconfiguration are amortised over a longer period of time. Partial reconfiguration aims to allow part of the device to be reprogrammed while another part is still performing active computation. Partial reconfiguration allows smaller reconfigurable bit streams thus not wasting energy on transmitting redundant information in the bit stream. Compression of the bit stream is possible but careful analysis is to be carried out to insure that the energy saved by using smaller bit streams is not outweighed by the computation needed to decompress the data.

Host coupling

Often the reconfigurable array is used as a processing accelerator attached to a host processor. The level of coupling determines the type of data transfers, latency, power, throughput and overheads involved when utilising the reconfigurable logic. Some of the most intuitive designs use a peripheral bus to provide a coprocessor like arrangement for the reconfigurable array. However, there have also been implementations where the reconfigurable fabric is much closer to the processor, some are even implemented into the data path, utilising the processor registers. The job of the host processor is to perform the control functions, configure the logic, schedule data and to provide external interfacing.


The flexibility in reconfigurable devices mainly comes from their routing interconnect. One style of interconnect made popular by FPGAs vendors, Xilinx and Altera are the island style layout, where blocks are arranged in an array with vertical and horizontal routing. A layout with inadequate routing may suffer from poor flexibility and resource utilisation, therefore providing limited performance. If too much interconnect is provided this requires more transistors than necessary and thus more silicon area, longer wires and more power consumption.

Tool flow

Generally, tools for configurable computing systems can be split up in two parts, CAD tools for reconfigurable array and compilation tools for CPU. The front-end compiler is an integrated tool, and will generate a structural hardware representation that is input of hardware design flow. Hardware design flow for reconfigurable architecture can be classified by the approach adopted by three main stages of design process: technology mapping, placement algorithm and routing algorithm. The software frameworks differ in the level of the programming language.

Some types of reconfigurable computers are microcoded processors where the microcode is stored in RAM or EEPROM, and changeable on reboot or on the fly. This could be done with the AMD 2900 series bit slice processors (on reboot) and later with FPGAs (on the fly).

Some dataflow processors are implemented using reconfigurable computing.

To compare the effect of various ways to implement an algorithm on the runtime and energy used, some tools allow compiling the same piece of C code for a fixed CPU, a soft processor, or compiling directly to FPGA

Reconfigurable computing as a paradigm shift: Hartenstein's anti machine

Table 1: Nick Tredennick’s Paradigm Classification Scheme
Early Historic Computers:
  Programming Source
Resources fixed none
Algorithms fixed none
von Neumann Computer:
  Programming Source
Resources fixed none
Algorithms variable Software (instruction streams)
Reconfigurable Computing Systems:
  Programming Source
Resources variable Configware (configuration)
Algorithms variable Flowware (data streams)

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 describes a Reconfigurable Computing Paradox: Software to configware migration (software to FPGA migration) results in reported speed-up factors of up to almost four orders of magnitude, as well as a reduction in electricity consumption by more than one order of magnitude---although the technological parameters of FPGA's 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 due to a paradigm shift, and is also partly explained by the Von Neumann syndrome.

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") .

The fundamental model of a Reconfigurable Computing Machine, the data-stream-based anti machine (also called Xputer), is the counterpart of the instruction-stream-based von Neumann machine paradigm. This is illustrated by a simple reconfigurable system (not dynamically reconfigurable), which has no instruction fetch at run time. The reconfiguration (before run time) can be considered as a kind of super instruction fetch. An anti machine does not have a program counter. The anti machine has data counters instead, since it is data-stream-driven. Here the definition of the term data streams is adopted from the systolic array scene, which defines, at which time which data item has to enter or leave which port, here of the reconfigurable system, which may be fine-grained (e. g. using FPGAs) or coarse-grained, or a mixture of both.

The systolic array scene, originally (early 1980s) mainly mathematicians, only defined one half of the anti machine: the data path: the Systolic array (also see Super Systolic Array). But they did not define nor model the data sequencer methodology, considering that this is not their job to take care where the data streams come from or end up. The data sequencing part of the anti machine is modeled as distributed memory, preferably on chip, which consists of auto-sequencing memory blocks (ASM blocks). Each ASM block has a sequencer including a data counter. An example is the Generic Address Generator (GAG), which is a generalization of the DMA.


References (not yet linked to text)

  • S. Hauck and A. DeHon, Reconfigurable Computing: The Theory and Practice of FPGA-Based Computing, Morgan Kaufman, 2008.
  • J. Henkel, S. Parameswaran (editors): Designing Embedded Processors. A Low Power Perspective; Springer Verlag, March 2007
  • J. Teich (editor) et al.: Reconfigurable Computing Systems. Special Topic Issue of Journal it - Information Technology, Oldenbourg Verlag, Munich. Vol. 49(2007) Issue 3
  • T.J. Todman, G.A. Constantinides, S.J.E. Wilton, O. Mencer, W. Luk and P.Y.K. Cheung, "Reconfigurable Computing: Architectures and Design Methods", IEE Proceedings: Computer & Digital Techniques, Vol. 152, No. 2, March 2005, pp. 193–208.
  • A. Zomaya (editor): Handbook of Nature-Inspired and Innovative Computing: Integrating Classical Models with Emerging Technologies; Springer Verlag, 2006

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