Streaming Elements for FPGA Signal and Image Processing Accelerators

Peng Wang, John McAllister

Research output: Contribution to journalArticle

12 Citations (Scopus)
427 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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