Abstract: Aspects of a method and system for digital signal processing within an adaptive computing engine are described. These aspects include a mini-matrix, the mini-matrix comprising a set of composite blocks, each composite block capable of executing a predetermined set of instructions. A sequencer is included for controlling the set of composite blocks and directing instructions among the set of composite blocks based on a data-flow graph. Further, a data network is included and transmits data to and from the set of composite blocks and to the sequencer, while a status network routes status word data resulting from instruction execution in the set of composite blocks. With the present invention, an effective combination of hardware resources is provided in a manner that provides multi-bit digital signal processing capabilities for an embedded system environment, particularly in an implementation of an adaptive computing engine.

Abstract: The method, system and tangible medium storing computer readable software of the present invention, provide for program constructs, such as commands, declarations, variables, and statements, which have been developed to describe computations for an adaptive computing architecture, rather than provide instructions to a sequential microprocessor or DSP architecture. The invention includes program constructs that permit a programmer to define data flow graphs in software, to provide for operations to be executed in parallel, and to reference variable states and historical values in a straightforward manner. The preferred method, system, and software also includes mechanisms for efficiently referencing array variables, and enables the programmer to succinctly describe the direct data flow among matrices, nodes, and other configurations of computational elements and computational units forming the adaptive computing architecture.

Abstract: A hardware task manager for managing operations in an adaptive computing system. The task manager indicates when input and output buffer resources are sufficient to allow a task to execute. The task can require an arbitrary number of input values from one or more other (or the same) tasks. Likewise, a number of output buffers must also be available before the task can start to execute and store results in the output buffers. The hardware task manager maintains a counter in association with each input and output buffer. For input buffers, a negative value for the counter means that there is no data in the buffer and, hence, the respective input buffer is not ready or available. Thus, the associated task can not run. Predetermined numbers of bytes, or “units,” are stored into the input buffer and an associated counter is incremented.

Abstract: A computational unit, or node, in an adaptive computing engine uses a uniform interface to a network to communicate with other nodes and resources. The uniform interface is referred to as a “node wrapper.” The node wrapper includes a hardware task manager (HTM), a data distributor, optional direct memory access (DMA) engine and a data aggregator. The hardware task manager indicates when input and output buffer resources are sufficient to allow a task to execute. The HTM coordinates a nodes assigned tasks using a task lists. A “ready-to-run queue” is implemented as a first-in first-out stack. The HTM uses a top-level finite-state machine (FSM) that communicates with a number of subordinate FSMs to control individual HTM components. The Data Distributor interfaces between the node's input pipeline register and various memories and registers within the node.

Abstract: The method, system and programming language of the present invention, provide for program constructs, such as commands, declarations, variables, and statements, which have been developed to describe computations for an adaptive computing architecture, rather than provide instructions to a sequential microprocessor or DSP architecture. The invention includes program constructs that permit a programmer to define data flow graphs in software, to provide for operations to be executed in parallel, and to reference variable states and historical values in a straightforward manner. The preferred method, system, and programming language also includes mechanisms for efficiently referencing array variables, and enables the programmer to succinctly describe the direct data flow among matrices, nodes, and other configurations of computational elements and computational units forming the adaptive computing architecture.

Abstract: Aspects of a method and system for digital signal processing within an adaptive computing engine are described. These aspects include a mini-matrix, the mini-matrix comprising a set of composite blocks, each composite block capable of executing a predetermined set of instructions. A sequencer is included for controlling the set of composite blocks and directing instructions among the set of composite blocks based on a data-flow graph. Further, a data network is included and transmits data to and from the set of composite blocks and to the sequencer, while a status network routes status word data resulting from instruction execution in the set of composite blocks. With the present invention, an effective combination of hardware resources is provided in a manner that provides multi-bit digital signal processing capabilities for an embedded system environment, particularly in an implementation of an adaptive computing engine.

Abstract: Aspects of a method and system for encoding instructions as a very long instruction word for processing in a plurality of computation units that reduces instruction memory requirements in a processing system are described. The aspects include determining at which stages of instruction processing that an instruction code needs to be executed. Further, an enable signal of the instruction code is utilized to direct execution during the determined stages by controlling storage operations for the instruction code.

Abstract: Aspects of a scheduler for an adaptable computing engine are described. The aspects include providing a plurality of computation units as hardware resources available to perform a particular segment of an assembled program on an adaptable computing engine. A schedule for the particular segment is refined by allocating the plurality of computation units in correspondence with a dataflow graph that represents the particular segment in an interactive manner until a feasible schedule is achieved.