The present invention concerns a new category of integrated circuitry and a new methodology for adaptive or reconfigurable computing. The preferred IC embodiment includes a plurality of heterogeneous computational elements coupled to an interconnection network. The plurality of heterogeneous computational elements include corresponding computational elements having fixed and differing architectures, such as fixed architectures for different functions such as memory, addition, multiplication, complex multiplication, subtraction, configuration, reconfiguration, control, input, output, and field programmability. In response to configuration information, the interconnection network is operative in real-time to configure and reconfigure the plurality of heterogeneous computational elements for a plurality of different functional modes, including linear algorithmic operations, non-linear algorithmic operations, finite state machine operations, memory operations, and bit-level manipulations. The various fixed architectures are selected to comparatively minimize power consumption and increase performance of the adaptive computing integrated circuit, particularly suitable for mobile, hand-held or other battery-powered computing applications.

Adaptive integrated circuitry with heterogeneous and reconfigurable matrices of diverse and adaptive computational units having fixed, application specific computational elements

The present invention concerns configuration of a new category of integrated circuitry for adaptive computing. The various embodiments provide an executable information module for an adaptive computing engine (ACE) integrated circuit and may include configuration information, operand data, and may also include routing and power control information. The ACE IC comprises a plurality of heterogeneous computational elements coupled to an interconnection network. The plurality of heterogeneous computational elements include corresponding computational elements having fixed and differing architectures, such as fixed architectures for different functions such as memory, addition, multiplication, complex multiplication, subtraction, configuration, reconfiguration, control, input, output, and field programmability. In response to configuration information, the interconnection network is operative to configure the plurality of heterogeneous computational elements for a plurality of different functional modes.

Apparatus, method, system and executable module for configuration and operation of adaptive integrated circuitry having fixed, application specific computational elements

Task definitions are used by a task scheduler and prioritizer to allocate task operations to a plurality of processing units. The task definition is an electronic record that specifies resources needed by, and other characteristics of, a task to be executed. Resources include types of processing nodes desired to execute the task, needed amount or rate of processing cycles, amount of memory capacity, number of registers, input/output ports, buffer sizes, etc. Characteristics of a task in clued maximum latency time, frequency of execution of a task, communication ports, and other characteristics. An examplary task definition language and syntax is described that uses constructs including order of attempted scheduling operations, percentage or amount of resources desired by different operations, handling of multiple executable images or modules, overlays, port aliases and other features.

Task definition for specifying resource requirements

A system for processing applications includes processor nodes and links interconnecting the processor nodes. Each node includes a processing element, a software extensible device, and a communication interface. The processing element executes at least one of the applications. The software extensible device provides additional instructions to a set of standard instructions for the processing element. The communication interface communicates with other processor nodes.

Systems and methods for software extensible multi-processing

Evolvable Hardware arises as a promising solution for automatic digital synthesis of digital and analog circuits. During the last decade, a special interest has been focused on evolving digital systems by directly mapping a chromosome on the FPGA configuration bitstream. This approach allowed a great degree of flexibility for evolving circuits. Nowadays, FPGAs routing scheme does not allow doing it in such flexible and safe way, so additional constraints must be introduced. In this paper we summarize three techniques for performing hardware evolution by exploiting the capacities of Virtex families. Among our proposals there are high and low level approaches, and coarse and fine grained components. A modular based evolution, with pre-placed and routed components, provides a coarse grain approach. Two techniques for directly modifying LUT contents on hard macros provide a fine grained evolution. Finally, integrating both approaches, coarse and fine grain, provides a more general and powerful framework.

Evolving hardware by dynamically reconfiguring xilinx FPGAs

MorphoSys is a system-on-chip which combines a RISC processor with an array of reconfigurable cells. The important features of MorphoSys are coarse-grain granularity dynamic reconfigurability and considerable depth of programmability. The first implementation of the MorphoSys architecture, the M1 chip, is currently at an advanced stage and it will operate at 100 MHz. Simulation results indicate significant performance improvements for different classes of applications, as compared to general-purpose processors. Meanwhile, MorphoSys can provide the potential hardware platforn for the evolvable hardware (EH) simulation with the help of the software.

V. Castro-Alves

Papers

The MorphoSys dynamically reconfigurable system-on-chip