Abstract.Compaan is a software tool that is capable of automatically translating nested loop programs, written in Matlab, into parallel process network descriptions suitable for implementation in hardware. In this article, we show a methodology and tool to convert these process networks into FPGA implementations. We will show that we can in principle obtain high performing realizations in a fraction of the design time currently employed to realize a parameterized implementation. This allows us to rapidly explore a range of transformations, such as loop unrolling and skewing, to generate a circuit that meets the requirements of a particular application. The QR decomposition algorithm is used to demonstrate the capability of the tool. We present results showing how the number of clock cycles and calculations-persecond vary with these transformations using a simple implementation of the function units. We also provide an indication of what we expect to achieve in the near future once the tools are completed and applied the transformations to parallel, highly pipelined implementations of the function units.

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We compare two parallel urray architectures for adaptive weight calculation based on QR-decomposition by Givens Rotations. We present FPGA implementations of borh orchitectures und compare them with un ASIC-bused solution. The throughput of the FPGA implementations is of the order 5-20 GigaFLOPS, making FPGA a viable alternative to ASIC implementation in applications where power consumption and volume cost ure not critical.

Current rapid synthesis approaches for reusable dedicated hardware components (cores) for digital signal processing systems are ineffective since they fail to capture and exploit the manner in which the resulting components are used as part of a heterogeneous system. This leads to counter-productive core redesign for each use of the core. This paper presents a solution to this issue which combines a novel but intuitive system modeling technique and associated core generation and integration methodology which generates reuable core architectures which may be optimised via algorithm level transformations. For an example design problem, these provide an effective rapid core synthesis and implementation exploration flow which allows a factor 3.9 throughput increase with no extra hardware expense.

Adaptive beamforming can play an important role in sensor array systems in countering directional interference. In highsample rate systems, such as radar and comms, the calculation of adaptive weights is a very computational task that requires highly parallel solutions. For systems where low power consumption and volume are important the only viable implementation is as an Application Specific Integrated Circuit (ASIC). However, the rapid advancement of Field Programmable Gate Array (FPGA) technology is enabling highly credible re-programmable solutions. In this paper we present the implementation of a scalable linear array processor for weight calculation using QR decomposition. We employ floating-point arithmetic with mantissa size optimised to the target application to minimise component size, and implement them as relationally placed macros (RPMs) on Xilinx Virtex FPGAs to achieve predictable dense layout and high-speed operation. We present results that show that 20GFLOPS of sustained computation on a single XCV3200E-8 Virtex-E FPGA is possible. We also describe the parameterised implementation of the floating-point operators and QR-processor, and the design methodology that enables us to rapidly generate complex FPGA implementations using the industry standard hardware description language VHDL.