High-level synthesis (HLS) is increasingly popular for the design of high-performance and energy-efficient heterogeneous systems, shortening time-to-market and addressing today’s system complexity. HLS allows designers to work at a higher-level of abstraction by using a software program to specify the hardware functionality. Additionally, HLS is particularly interesting for designing field-programmable gate array circuits, where hardware implementations can be easily refined and replaced in the target device. Recent years have seen much activity in the HLS research community, with a plethora of HLS tool offerings, from both industry and academia. All these tools may have different input languages, perform different internal optimizations, and produce results of different quality, even for the very same input description. Hence, it is challenging to compare their performance and understand which is the best for the hardware to be implemented. We present a comprehensive analysis of recent HLS tools, as well as overview the areas of active interest in the HLS research community. We also present a first-published methodology to evaluate different HLS tools. We use our methodology to compare one commercial and three academic tools on a common set of C benchmarks, aiming at performing an in-depth evaluation in terms of performance and the use of resources.

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A Survey and Evaluation of FPGA High-Level Synthesis Tools

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

FPGAs are often used as implementation platforms for real-time image processing applications because their structure is able to exploit spatial and temporal parallelism. Such parallelisation is subject to the processing mode and hardware constraints of the system. These constraints can force the designer to reformulate the algorithm. This paper presents some general techniques for dealing with the various constraints and efficient mappings for three types of image processing operations.

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Implementing Image Processing Algorithms on FPGAs

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

RC systems typically consist of an array of configurable computing elements. The computational granularity of these elements ranges from simple gates - as abstracted by FPGA lookup tables - to complete arithmetic-logic units with or without registers. A rich programmable interconnect completes the array. RC system developer manually partitions an application into two segments: a hardware component in a hardware description language such as VHDL or Verilog that will execute as a circuit on the FPGA and a software component that will execute as a program on the host. Single-assignment C is a C language variant designed to create an automated compilation path from an algorithmic programming language to an FPGA-based reconfigurable computing system.

C. Ross

Papers

High-level language abstraction for reconfigurable computing