Daniel Vazquez | Searcharxiv

arXiv2026

YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception

The trustworthy object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here. The approach addresses a key limitation in computer vision for vehicle detection perception, and beyond. These systems offer limited transparency regarding the reliability of their confidence scores in visually degraded or ambiguous scenes. To this end, a Kolmogorov-Arnold network is employed as an interpretable post-hoc surrogate to model the trustworthiness of the You Only Look Once (Yolov10) detections using seven geometric and semantic features. The additive spline-based structure of the Kolmogorov-Arnold network enables direct visualisation of each feature's influence. This produces smooth and transparent functional mappings that reveal when the model's confidence is well supported and when it is unreliable. Furthermore, a bootstrapped language-image (BLIP) foundation model generates descriptive captions of each scene. This tool enables a lightweight multimodal interface without affecting the interpretability layer. Experiments on both Common Objects in Context (COCO), and images from the University of Bath campus demonstrate that the framework accurately identifies low-trust predictions under blur, occlusion, or low texture. This provides actionable insights for acceptance, review, or downstream risk mitigation. The resulting system delivers interpretable object detection with trustworthy confidence estimates. It offers a powerful tool for transparent and practical perception component for autonomous and multimodal artificial intelligence applications.

cs.CV↗

arXiv2024

STRELA: STReaming ELAstic CGRA Accelerator for Embedded Systems

Reconfigurable computing offers a good balance between flexibility and energy efficiency. When combined with software-programmable devices such as CPUs, it is possible to obtain higher performance by spatially distributing the parallelizable sections of an application throughout the reconfigurable device while the CPU is in charge of control-intensive sections. This work introduces an elastic Coarse-Grained Reconfigurable Architecture (CGRA) integrated into an energy-efficient RISC-V-based SoC designed for the embedded domain. The microarchitecture of CGRA supports conditionals and irregular loops, making it adaptable to domain-specific applications. Additionally, we propose specific mapping strategies that enable the efficient utilization of the CGRA for both simple applications, where the fabric is only reconfigured once (one-shot kernel), and more complex ones, where it is necessary to reconfigure the CGRA multiple times to complete them (multi-shot kernels). Large kernels also benefit from the independent memory nodes incorporated to streamline data accesses. Due to the integration of CGRA as an accelerator of the RISC-V processor enables a versatile and efficient framework, providing adaptability, processing capacity, and overall performance across various applications. The design has been implemented in TSMC 65 nm, achieving a maximum frequency of 250 MHz. It achieves a peak performance of 1.22 GOPs computing one-shot kernels and 1.17 GOPs computing multi-shot kernels. The best energy efficiency is 72.68 MOPs/mW for one-shot kernels and 115.96 MOPs/mW for multi-shot kernels. The design integrates power and clock-gating techniques to tailor the architecture to the embedded domain while maintaining performance. The best speed-ups are 17.63x and 18.61x for one-shot and multi-shot kernels. The best energy savings in the SoC are 9.05x and 11.10x for one-shot and multi-shot kernels.

cs.AR↗