Georgios Floros

Eashan Wadhwa, Georgios Floros, Shanker Shreejith

Pre-implementation behavioural simulation routinely validates functional correctness, yet it also produces rich switching-activity traces that are typically discarded by FPGA computer-aided design (CAD) flows. Prior simulation-guided and power-aware FPGA optimisations demonstrate the promise of exploiting this metadata, but many rely on fixed thresholds, narrow decision heuristics, or limited design awareness, often incurring substantial area overhead. This paper presents Context-aware Simopt-Power, a simulator-guided optimisation framework that combines activity metadata with lightweight structural features (sequential proximity, logic-depth proxies, and fan-out estimates) to more precisely target high-impact regions of the netlist. We additionally remove empirically tuned constants, replacing them with architecture-aware parameters such as LUT size and mapping constraints, and evaluate trade-offs using power, delay, and a more useful metrics, area-delay product (AD) and power-delay product (PD). Implemented in an open-source Yosys/ABC flow and evaluated on the complex Koios deep-learning accelerator benchmarks, Context-aware Simopt-Power achieves an average 6.8% dynamic-power reduction while limiting LUT overhead to 11.2%, thus enabling a holistic design optimisation.

Panagiotis Sapoutzoglou, George Terzakis, Georgios Floros et al.

Traditional explicit 3D representations, such as point clouds and meshes, demand significant storage to capture fine geometric details and require complex indexing systems for surface lookups, making functional representations an efficient, compact, and continuous alternative. In this work, we propose a novel, object-specific functional shape representation that models surface geometry with Gaussian Process (GP) mixture models. Rather than relying on computationally heavy neural architectures, our method is lightweight, leveraging GPs to learn continuous directional distance fields from sparsely sampled point clouds. We capture complex topologies by anchoring local GP priors at strategic reference points, which can be flexibly extracted using any structural decomposition method (e.g. skeletonization, distance-based clustering). Extensive evaluations on the ShapeNetCore and IndustryShapes datasets demonstrate that our method can efficiently and accurately represent complex geometries.

Panagiotis Sapoutzoglou, Georgios Giapitzakis, Georgios Floros et al.

We propose a generic procedure for assessing 6D object pose estimates. Our approach relies on the evaluation of discrepancies in the geometry of the observed object, in particular its respective estimated back-projection in 3D, against a putative functional shape representation comprising mixtures of Gaussian Processes, that act as a template. Each Gaussian Process is trained to yield a fragment of the object's surface in a radial fashion with respect to designated reference points. We further define a pose confidence measure as the average probability of pixel back-projections in the Gaussian mixture. The goal of our experiments is two-fold. a) We demonstrate that our functional representation is sufficiently accurate as a shape template on which the probability of back-projected object points can be evaluated, and, b) we show that the resulting confidence scores based on these probabilities are indeed a consistent quality measure of pose.