Streaming Elements for FPGA Signal and Image Processing Accelerators

Peng Wang, John McAllister

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
604 Downloads (Pure)


Field programmable gate array devices boast abundant resources with which custom accelerator components for signal, image and data processing may be realised; however, realising high performance, low cost accelerators currently demands manual register transfer level design. Software-programmable ’soft’ processors have been proposed as a way to reduce this design burden but they are unable to support performance and cost comparable to custom circuits. This paper proposes a new soft processing approach for FPGA which promises to overcome this barrier. A high performance, fine-grained streaming processor, known as a Streaming Accelerator Element, is proposed which realises accelerators as large scale custom multicore networks. By adopting a streaming execution approach with advanced program control and memory addressing capabilities, typical program inefficiencies can be almost completely eliminated to enable performance and cost which are unprecedented amongst software-programmable solutions. When used to realise accelerators for fast fourier transform, motion estimation, matrix multiplication and sobel edge detection it is shown how the proposed architecture enables real-time performance and with performance and cost comparable with hand-crafted custom circuit accelerators and up to two orders of magnitude beyond existing soft processors.
Original languageEnglish
Pages (from-to)2262-2274
Number of pages13
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Issue number6
Early online date06 Jan 2016
Publication statusEarly online date - 06 Jan 2016


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