Zero-LUT Open-Set Plankton Classification on Edge MPSoC and GPU Platforms
Year: 2026
Type: article
Abstract: Autonomous plankton monitoring needs classifiers that run on low-power edge hardware and reject out-of-distribution (OOD) inputs. Our prior in situ plankton image biomass estimation (IsPlanktonBIO) framework identifies organisms by nearest-neighbour search over a similarity gallery, a memory-resident lookup that stalls 8-bit integer (INT8) acceleration and grows with the corpus. We replace it with a zero look-up-table (Zero-LUT) linear energy head: a matrix-multiply-only scorer over a supervised-contrastive backbone that needs no database and shrinks the footprint sevenfold, to 1.3 MB. We assess recognition and rejection jointly with an open-set protocol based on the correct-classification rate at a fixed OOD-leakage budget, showing that closed-set accuracy alone misleads. Our hardware–software co-design on an AMD Kria KV260 Multiprocessor System-on-Chip (MPSoC) runs the INT8 pipeline in real time at 86.5% end-to-end accuracy; its calibrated gate accepts 82.9% of in-distribution images, rejects 89.0% of OOD inputs, and correctly labels 97.6% of those accepted. INT8 conversion raises two issues: classification varies for background-dominated patterns, and OOD thresholds must be recalibrated on the compressed model, not inherited from full precision. An NVIDIA Jetson Orin Nano Super port reproduces this behaviour: the GPU is faster and more energy-efficient, whereas the MPSoC’s reconfigurable logic can host sensing and control beside the accelerator.
Source: Electronics
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7204891735