arXiv subjects

Denis Hoornaert

Publications and source records attributed to Denis Hoornaert.

6 recordsLinked to original sources

arXiv2026

Tensor Memory Engine: On-the-fly Data Reorganization for Ideal Locality

The shift to data-intensive processing from the cloud to the edge has introduced new challenges and expectations for the next generation of intelligent computing systems. As the memory wall continues to grow, modern systems can only meet these performance expectations by displaying data access patterns that exhibit ideal layouts in memory and ideal spatiotemporal locality in caches. However, only a few data-intensive applications are characterized by ideal locality. Instead, most applications exhibit either (i) poor locality when naively implemented and must undergo costly redesigns and tuning or (ii) inflated memory footprint to offer proper locality. To address the aforementioned challenges, we propose a hardware/software co-designed approach that can be implemented on commercially available SoC/FPGA platforms. Our approach seamlessly inserts in the CPUs' data path a Tensor Memory Engine that provides data with an ideal cache locality to running applications by (i) accessing the memory on behalf of the CPUs and (ii) composing a re-organized view of the memory layout. Unlike in- and near-memory computing approaches, it sets itself apart by clearly decoupling computing and memory accesses; computation is still performed on CPUs while the data re-organization is delegated to the Tensor Memory Engine.

cs.AR↗

arXiv2026

Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization

Residual policy learning (RPL), in which a learned policy refines a static base policy using deep reinforcement learning (DRL), has shown strong performance across various robotic applications. Its effectiveness is particularly evident in autonomous racing, a domain that serves as a challenging benchmark for real-world DRL. However, deploying RPL-based controllers introduces system complexity and increases inference latency. We address this by introducing an extension of RPL named attenuated residual policy optimization ($\alpha$-RPO). Unlike standard RPL, $\alpha$-RPO yields a standalone neural policy by progressively attenuating the base policy, which initially serves to bootstrap learning. Furthermore, this mechanism enables a form of privileged learning, where the base policy is permitted to use sensor modalities not required for final deployment. We design $\alpha$-RPO to integrate seamlessly with PPO, ensuring that the attenuated influence of the base controller is dynamically compensated during policy optimization. We evaluate $\alpha$-RPO by building a framework for 1:10-scaled autonomous racing around it. In both simulation and zero-shot real-world transfer to Roboracer cars, $\alpha$-RPO not only reduces system complexity but also improves driving performance compared to baselines - demonstrating its practicality for robotic deployment. Our code is available at: https://github.com/raphajaner/arpo_racing.

cs.RO↗

arXiv2025

Light Virtualization: a proof-of-concept for hardware-based virtualization

Virtualization has become widespread across all computing environments, from edge devices to cloud systems. Its main advantages are resource management through abstraction and improved isolation of platform resources and processes. However, there are still some important tradeoffs as it requires significant support from the existing hardware infrastructure and negatively impacts performance. Additionally, the current approaches to resource virtualization are inflexible, using a model that doesn't allow for dynamic adjustments during operation. This research introduces Light Virtualization (LightV), a new virtualization method for commercial platforms. LightV uses programmable hardware to direct cache coherence traffic, enabling precise and seamless control over which resources are virtualized. The paper explains the core principles of LightV, explores its capabilities, and shares initial findings from a basic proof-of-concept module tested on commercial hardware.

cs.DC↗

arXiv2023

Residual Policy Learning for Vehicle Control of Autonomous Racing Cars

The development of vehicle controllers for autonomous racing is challenging because racing cars operate at their physical driving limit. Prompted by the demand for improved performance, autonomous racing research has seen the proliferation of machine learning-based controllers. While these approaches show competitive performance, their practical applicability is often limited. Residual policy learning promises to mitigate this drawback by combining classical controllers with learned residual controllers. The critical advantage of residual controllers is their high adaptability parallel to the classical controller's stable behavior. We propose a residual vehicle controller for autonomous racing cars that learns to amend a classical controller for the path-following of racing lines. In an extensive study, performance gains of our approach are evaluated for a simulated car of the F1TENTH autonomous racing series. The evaluation for twelve replicated real-world racetracks shows that the residual controller reduces lap times by an average of 4.55 % compared to a classical controller and even enables lap time gains on unknown racetracks.

cs.RO↗

arXiv2022

RT-Bench: an Extensible Benchmark Framework for the Analysis and Management of Real-Time Applications

Benchmarking is crucial for testing and validating any system, even more so in real-time systems. Typical real-time applications adhere to well-understood abstractions: they exhibit a periodic behavior, operate on a well-defined working set, and strive for stable response time avoiding non-predicable factors such as page faults. Unfortunately, available benchmark suites fail to reflect key characteristics of real-time applications. Practitioners and researchers must resort to either benchmark heavily approximated real-time environments, or to re-engineer available benchmarks to add -- if possible -- the sought-after features. Additionally, the measuring and logging capabilities provided by most benchmark suites are not tailored "out-of-the-box" to real-time environments, and changing basic parameters such as the scheduling policy often becomes a tiring and error-prone exercise. In this paper, we present RT-bench, an open-source framework adding standard real-time features to virtually any existing benchmark. Furthermore, RT-bench provides an easy-to-use, unified command line interface to customize key aspects of the real-time execution of a set of benchmarks. Our framework is guided by four main criteria: 1) cohesive interface, 2) support for periodic application behavior and deadline semantics, 3) controllable memory footprint, and 4) extensibility and portability. We have integrated within the framework applications from the widely used SD-VBS and IsolBench suites. We showcase a set of use-cases that are representative of typical real-time system evaluation scenarios and that can be easily conducted via RT-Bench.

cs.SE↗

arXiv2022

Relational Memory: Native In-Memory Accesses on Rows and Columns

Analytical database systems are typically designed to use a column-first data layout to access only the desired fields. On the other hand, storing data row-first works great for accessing, inserting, or updating entire rows. Transforming rows to columns at runtime is expensive, hence, many analytical systems ingest data in row-first form and transform it in the background to columns to facilitate future analytical queries. How will this design change if we can always efficiently access only the desired set of columns? To address this question, we present a radically new approach to data transformation from rows to columns. We build upon recent advancements in embedded platforms with re-programmable logic to design native in-memory access on rows and columns. Our approach, termed Relational Memory, relies on an FPGA- based accelerator that sits between the CPU and main memory and transparently transforms base data to any group of columns with minimal overhead at runtime. This design allows accessing any group of columns as if it already exists in memory. We implement and deploy Relational Memory in real hardware, and we show that we can access the desired columns up to 1.63x faster than accessing them from their row-wise counterpart, while matching the performance of a pure columnar access for low projectivity, and outperforming it by up to 1.87x as projectivity (and tuple re-construction cost) increases. Moreover, our approach can be easily extended to support offloading of a number of operations to hardware, e.g., selection, group by, aggregation, and joins, having the potential to vastly simplify the software logic and accelerate the query execution.

cs.DB↗