Programmable embedded platforms for remote and compute intensive image processing applications
Lead Research Organisation:
Heriot-Watt University
Department Name: S of Mathematical and Computer Sciences
Abstract
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Organisations
Publications
Stewart R
(2018)
RIPL: A Parallel Image Processing Language for FPGAs
in ACM Transactions on Reconfigurable Technology and Systems
Stewart Robert
(2015)
RIPL: An Efficient Image Processing DSL for FPGAs
in arXiv e-prints
Baisa N
(2019)
Multiple target, multiple type filtering in the RFS framework
in Digital Signal Processing
Bhowmik D
(2016)
Visual Attention-Based Image Watermarking
in IEEE Access
Wu Y
(2022)
Energy Efficient Approximate 3D Image Reconstruction
in IEEE Transactions on Emerging Topics in Computing
STEWART R
(2016)
Transparent fault tolerance for scalable functional computation
in Journal of Functional Programming
Garcia P
(2019)
Optimized Memory Allocation and Power Minimization for FPGA-Based Image Processing.
in Journal of imaging
Archibald B
(2018)
Replicable parallel branch and bound search
in Journal of Parallel and Distributed Computing
Stewart R
(2015)
Profile Guided Dataflow Transformation for FPGAs and CPUs
in Journal of Signal Processing Systems
Stewart R
(2019)
Verifying parallel dataflow transformations with model checking and its application to FPGAs
in Journal of Systems Architecture
Baisa N
(2018)
Long-term correlation tracking using multi-layer hybrid features in sparse and dense environments
in Journal of Visual Communication and Image Representation
Baisa N
(2019)
Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking
in Journal of Visual Communication and Image Representation
Maeir, P.
(2014)
The HDPH DSLs for Scalable, Reliable Computation
in Proceedings of Haskell Symposium
Maier P
(2014)
The HdpH DSLs for scalable reliable computation
Garcia P
(2017)
Learning to Approximate Computing at Run-time
Stewart R
(2016)
An Image Processing Language
Archibald B
(2017)
Towards Generic Scalable Parallel Combinatorial Search
Bhowmik D
(2018)
Statistical T+2d Subband Modelling for Crowd Counting
Asmann A
(2021)
Deep Learning for LiDAR Waveforms with Multiple Returns
Hamadouche A
(2021)
Approximate Proximal-Gradient Methods
| Description | Reconfigurable hardware (Field Programmable Gate Arrays aka FPGAs) offer strong opportunities for efficient implementation of complex algorithms, in particular for image processing, but are very hard for non-experts to deploy effectively. We have elaborated an approach based in a very high level domain specific language (DSL) where the programmer may focus on expressing the algorithm and a rich toolset will then aid with mapping it to the FPGA. Our key outcomes are: a) a methodology for representation of Streaming Image Processing algorithms by dataflow abstractions, allowing code transformation and restructuring leading to improved performance on both FPGA and CPU architectures b) the RIPL DSL c) substantial exemplars across the spectrum of image processing in RIPL d) a machine assisted tool for exploring different configurations of RIPL programs driven by precise performance information e) design and implementation of a smart camera architecture f) evaluation of our approach on real-time image processing algorithms on an FPGA against traditional hand-crafted code g) dissemination to a wide academic and industrial audience h) foundation of a growing international workshop series on DSLs, now in its 4th year. |
| Exploitation Route | Our framework for resource driven refactoring based on dataflow should be more widely applicable to other architectures (e.g. many-core CPU, GPU) and to heterogeneous platforms that combine these. |
| Sectors | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Electronics,Energy,Transport |
| URL | http://rathlin.hw.ac.uk/ |
| Description | As part of our ongoing programme investigating the implementation of computer imaging and vision algorithms in embedded hardware, we have presented our findings in a number of collaborative industry-academia themed and knowledge transfer meetings under the auspices of the "Signal Processing for the Information Age" programme. Industrial partners include Mathworks, Seebyte, Atlas Elektronik, Cubicon, ADS, Kaon, BAE Systems, Leonardo, Thales, Qinetiq, and Roke Manor. |
| First Year Of Impact | 2018 |
| Sector | Aerospace, Defence and Marine |
| Description | Higher Education Impact Fellowship (HEIF) on low power and accelerated image processing hardware development |
| Amount | £4,500 (GBP) |
| Organisation | Sheffield Hallam University |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 07/2017 |
| End | 06/2018 |
| Description | Match funded PhD studentship on 'Domain specific optimisations for real-time image processing on heterogeneous hardware' |
| Amount | £143,000 (GBP) |
| Organisation | ST Microelectronics |
| Sector | Private |
| Country | Switzerland |
| Start | 10/2019 |
| End | 09/2022 |
| Description | Signal Procssing in the Information Age |
| Amount | £4,092,207 (GBP) |
| Funding ID | EP/S000631/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 07/2018 |
| End | 03/2024 |