Data · dataset · 2026
FPGA-based programmable embedded platform for image processing applications
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32863013.v1
A vast majority of electronic systems including medical, surveillance and critical infrastructure employs image processing to provide intelligent analysis.
Description
They use onboard pre-processing to reduce data bandwidth and memory requirements before sending information to the central system. Field Programmable Gate Arrays (FPGAs) represent a strong platform as they permit reconfigurability and pipelining for streaming applications.
However, rapid advances and changes in these application use cases crave adaptable hardware architectures that can process dynamic data workloads and be easily programmed to achieve ecient solutions in terms of area, time and power.<br><br>FPGA-based development needs iterative design cycles, hardware synthesis and place-and-route times which are alien to the software developers. This work proposes an FPGA-based programmable hardware acceleration approach to reduce design effort and time.
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This allows developers to use FPGAs to profile, optimise and quickly prototype algorithms using a more familiar software-centric, edit-compile-run design flow that enables the programming of the platform by software rather than high-level synthesis (HLS) engineering principles.<br><br>Central to the work has been the development of an optimised FPGA-based processor called Image Processing Processor (IPPro) which efficiently uses the underlying resources and presents a programmable environment to the programmer using a dataflow design principle.
This gives superior performance when compared to competing alternatives. From this, a three-layered platform has been created which enables the realisation of parallel computing skeletons on FPGA which are used to eciently express designs in high-level programming languages. From bottom-up, these layers represent programming (actor, multiple actors and parallel skeletons) and hardware (IPPro core, multicore IPPro, system infrastructure) abstraction.
The platform allows acceleration of parallel and non-parallel dataflow applications.<br><br>A set of point and area image pre-processing functions are implemented on Avnet Zedboard platform which allows the evaluation of the performance. The point function achieved 2.53 times better performance than the area functions and point and area functions achieved performance improvements of 7.80 and 5.27 times over sin- gle core IPPro by exploiting data parallelism.
The pipelined execution of multiple stages revealed that a dataflow graph can be decomposed into balanced actors to deliver maximum performance by hiding data transfer and processing time through exploiting task parallelism; otherwise, the maximum achievable performance is limited by the slowest actor due to the ripple effect caused by unbalanced actors. The platform delivered better performance in terms of fps/Watt/Area than Embedded Graphic Processing Unit (GPU) considering both technologies allows a software-centric design flow.<br>
Links
Where it is published
- DOI doi.org/10.17034/32863013.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Image processing · Image processing · Image processing
- Inferred from text
- Image 75%
Provenance · 3 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32863013 | 5 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32863013 | 5 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32863013 | 5 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:460306 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['Image Processing'] |
| concepts[field].anzsrc:field:460306 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['Image Processing'] |
| concepts[field].anzsrc:field:460306 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Image Processing'] |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |