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The CAPSARII Approach to Cyber-Secure Wearable, Ultra-Low-Power Networked Sensors for Soldier Health Monitoring
Authors:
Luciano Bozzi,
Christian Celidonio,
Umberto Nuzzi,
Massimo Biagini,
Stefano Cherubin,
Asbjørn Djupdal,
Tor Andre Haugdahl,
Andrea Aliverti,
Alessandra Angelucci,
Giovanni Agosta,
Gerardo Pelosi,
Paolo Belluco,
Samuele Polistina,
Riccardo Volpi,
Luigi Malagò,
Michael Schneider,
Florian Wieczorek,
Xabier Eguiluz
Abstract:
The European Defence Agency's revised Capability Development Plan (CDP) identifies as a priority improving ground combat capabilities by enhancing soldiers' equipment for better protection. The CAPSARII project proposes in innovative wearable system and Internet of Battlefield Things (IoBT) framework to monitor soldiers' physiological and psychological status, aiding tactical decisions and medical…
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The European Defence Agency's revised Capability Development Plan (CDP) identifies as a priority improving ground combat capabilities by enhancing soldiers' equipment for better protection. The CAPSARII project proposes in innovative wearable system and Internet of Battlefield Things (IoBT) framework to monitor soldiers' physiological and psychological status, aiding tactical decisions and medical support. The CAPSARII system will enhance situational awareness and operational effectiveness by monitoring physiological, movement and environmental parameters, providing real-time tactical decision support through AI models deployed on edge nodes and enable data analysis and comparative studies via cloud-based analytics. CAPSARII also aims at improving usability through smart textile integration, longer battery life, reducing energy consumption through software and hardware optimizations, and address security concerns with efficient encryption and strong authentication methods. This innovative approach aims to transform military operations by providing a robust, data-driven decision support tool.
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Submitted 8 February, 2026;
originally announced February 2026.
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Architecture, Simulation and Software Stack to Support Post-CMOS Accelerators: The ARCHYTAS Project
Authors:
Giovanni Agosta,
Stefano Cherubin,
Derek Christ,
Francesco Conti,
Asbjørn Djupdal,
Matthias Jung,
Georgios Keramidas,
Roberto Passerone,
Paolo Rech,
Elisa Ricci,
Philippe Velha,
Flavio Vella,
Kasim Sinan Yildirim,
Nils Wilbert
Abstract:
ARCHYTAS aims to design and evaluate non-conventional hardware accelerators, in particular, optoelectronic, volatile and non-volatile processing-in-memory, and neuromorphic, to tackle the power, efficiency, and scalability bottlenecks of AI with an emphasis on defense use cases (e.g., autonomous vehicles, surveillance drones, maritime and space platforms). In this paper, we present the system arch…
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ARCHYTAS aims to design and evaluate non-conventional hardware accelerators, in particular, optoelectronic, volatile and non-volatile processing-in-memory, and neuromorphic, to tackle the power, efficiency, and scalability bottlenecks of AI with an emphasis on defense use cases (e.g., autonomous vehicles, surveillance drones, maritime and space platforms). In this paper, we present the system architecture and software stack that ARCHYTAS will develop to integrate and support those accelerators, as well as the simulation software needed for early prototyping of the full system and its components.
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Submitted 18 October, 2025;
originally announced October 2025.
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4Hammer: a board-game reinforcement learning environment for the hour long time frame
Authors:
Massimo Fioravanti,
Giovanni Agosta
Abstract:
Large Language Models (LLMs) have demonstrated strong performance on tasks with short time frames, but struggle with tasks requiring longer durations. While datasets covering extended-duration tasks, such as software engineering tasks or video games, do exist, there are currently few implementations of complex board games specifically designed for reinforcement learning and LLM evaluation. To addr…
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Large Language Models (LLMs) have demonstrated strong performance on tasks with short time frames, but struggle with tasks requiring longer durations. While datasets covering extended-duration tasks, such as software engineering tasks or video games, do exist, there are currently few implementations of complex board games specifically designed for reinforcement learning and LLM evaluation. To address this gap, we propose the 4Hammer reinforcement learning environment, a digital twin simulation of a subset of Warhammer 40,000-a complex, zero-sum board game. Warhammer 40,000 features intricate rules, requiring human players to thoroughly read and understand over 50 pages of detailed natural language rules, grasp the interactions between their game pieces and those of their opponents, and independently track and communicate the evolving game state.
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Submitted 19 May, 2025;
originally announced May 2025.
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Rulebook: bringing co-routines to reinforcement learning environments
Authors:
Massimo Fioravanti,
Samuele Pasini,
Giovanni Agosta
Abstract:
Reinforcement learning (RL) algorithms, due to their reliance on external systems to learn from, require digital environments (e.g., simulators) with very simple interfaces, which in turn constrain significantly the implementation of such environments. In particular, these environments are implemented either as separate processes or as state machines, leading to synchronization and communication o…
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Reinforcement learning (RL) algorithms, due to their reliance on external systems to learn from, require digital environments (e.g., simulators) with very simple interfaces, which in turn constrain significantly the implementation of such environments. In particular, these environments are implemented either as separate processes or as state machines, leading to synchronization and communication overheads in the first case, and to unstructured programming in the second.
We propose a new domain-specific, co-routine-based, compiled language, called Rulebook, designed to automatically generate the state machine required to interact with machine learning (ML) algorithms and similar applications, with no performance overhead. Rulebook allows users to express programs without needing to be aware of the specific interface required by the ML components. By decoupling the execution model of the program from the syntactical encoding of the program, and thus without the need for manual state management, Rulebook allows to create larger and more sophisticated environments at a lower development cost.
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Submitted 28 April, 2025;
originally announced April 2025.
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Array-Aware Matching: Taming the Complexity of Large-Scale Simulation Models
Authors:
Massimo Fioravanti,
Daniele Cattaneo,
Federico Terraneo,
Silvano Seva,
Stefano Cherubin,
Giovanni Agosta,
Francesco Casella,
Alberto Leva
Abstract:
Equation-based modelling is a powerful approach to tame the complexity of large-scale simulation problems. Equation-based tools automatically translate models into imperative languages. When confronted with nowadays' problems, however, well assessed model translation techniques exhibit scalability issues, that are particularly severe when models contain very large arrays. In fact, such models can…
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Equation-based modelling is a powerful approach to tame the complexity of large-scale simulation problems. Equation-based tools automatically translate models into imperative languages. When confronted with nowadays' problems, however, well assessed model translation techniques exhibit scalability issues, that are particularly severe when models contain very large arrays. In fact, such models can be made very compact by enclosing equations into looping constructs, but reflecting the same compactness into the translated imperative code is not trivial. In this paper, we face this issue by concentrating on a key step of equations-to-code translation, the equation/variable matching. We first show that an efficient translation of models with (large) arrays needs awareness of their presence, by defining a figure of merit to measure how much the looping constructs are preserved along the translation. We then show that the said figure of merit allows to define an optimal array-aware matching, and as our main result, that the so stated optimal array-aware matching problem is NP-complete. As an additional result, we propose a heuristic algorithm capable of performing array-aware matching in polynomial time. The proposed algorithm can be proficiently used by model translator developers in the implementation of efficient tools for large-scale system simulation.
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Submitted 6 September, 2023; v1 submitted 22 November, 2022;
originally announced December 2022.
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The RECIPE Approach to Challenges in Deeply Heterogeneous High Performance Systems
Authors:
Giovanni Agosta,
William Fornaciari,
David Atienza,
Ramon Canal,
Alessandro Cilardo,
José Flich Cardo,
Carles Hernandez Luz,
Michal Kulczewski,
Giuseppe Massari,
Rafael Tornero Gavilá,
Marina Zapater
Abstract:
RECIPE (REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems) is a recently started project funded within the H2020 FETHPC programme, which is expressly targeted at exploring new High-Performance Computing (HPC) technologies. RECIPE aims at introducing a hierarchical runtime resource management infrastructure to optimize energy efficiency and minimize t…
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RECIPE (REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems) is a recently started project funded within the H2020 FETHPC programme, which is expressly targeted at exploring new High-Performance Computing (HPC) technologies. RECIPE aims at introducing a hierarchical runtime resource management infrastructure to optimize energy efficiency and minimize the occurrence of thermal hotspots, while enforcing the time constraints imposed by the applications and ensuring reliability for both time-critical and throughput-oriented computation that run on deeply heterogeneous accelerator-based systems. This paper presents a detailed overview of RECIPE, identifying the fundamental challenges as well as the key innovations addressed by the project. In particular, the need for predictive reliability approaches to maximize hardware lifetime and guarantee application performance is identified as the key concern for RECIPE, and is addressed via hierarchical resource management of the heterogeneous architectural components of the system, driven by estimates of the application latency and hardware reliability obtained respectively through timing analysis and modelling thermal properties, mean-time-to-failure of subsystems. We show the impact of prediction accuracy on the overheads imposed by the checkpointing policy, as well as a possible application to a weather forecasting use case.
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Submitted 4 March, 2021;
originally announced March 2021.
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Tunable Approximations to Control Time-to-Solution in an HPC Molecular Docking Mini-App
Authors:
Davide Gadioli,
Gianluca Palermo,
Stefano Cherubin,
Emanuele Vitali,
Giovanni Agosta,
Candida Manelfi,
Andrea R. Beccari,
Carlo Cavazzoni,
Nico Sanna,
Cristina Silvano
Abstract:
The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the three-dimensional pose of a given molecule when it interacts with the target protein binding site. This task is often done for virtual screening a huge set of molecules to find the most promising ones, which will be for…
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The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the three-dimensional pose of a given molecule when it interacts with the target protein binding site. This task is often done for virtual screening a huge set of molecules to find the most promising ones, which will be forwarded to the later stages of the drug discovery process. Given the huge complexity of the problem, molecular docking cannot be solved by exploring the entire space of the ligand poses. State-of-the-art approaches face the problem by sampling the space of the ligand poses to generate results in a reasonable time budget. In this work, we improve the geometric approach to molecular docking by introducing tunable approximations. In particular, we analyzed and enriched the original implementation with tunable software knobs to explore and control the performance-accuracy tradeoffs. We modeled time-to-solution of the virtual screening task as a function of software knobs, input data features, and available computational resources. Therefore, the application can autotune its configuration according to a user-defined time budget. We used a Mini-App derived by LiGenDock - a state-of-the-art molecular docking application - to validate the proposed approach. We run the enhanced Mini-App on an HPC system by using a very large database of pockets and ligands. The proposed approach exposes a time-to-solution interval spanning more than one order of magnitude with accuracy degradation up to 30%, more in general providing different accuracy levels according to the needs of the virtual screening campaign.
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Submitted 18 January, 2019;
originally announced January 2019.
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The ANTAREX Domain Specific Language for High Performance Computing
Authors:
Cristina Silvano,
Giovanni Agosta,
Andrea Bartolini,
Andrea R. Beccari,
Luca Benini,
Loïc Besnard,
João Bispo,
Radim Cmar,
João M. P. Cardoso,
Carlo Cavazzoni,
Daniele Cesarini,
Stefano Cherubin,
Federico Ficarelli,
Davide Gadioli,
Martin Golasowski,
Antonio Libri,
Jan Martinovič,
Gianluca Palermo,
Pedro Pinto,
Erven Rohou,
Kateřina Slaninová,
Emanuele Vitali
Abstract:
The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enfo…
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The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. In this paper, we present an overview of the key outcome of the project, the ANTAREX DSL, and some of its capabilities through a number of examples, including how the DSL is applied in the context of the project use cases.
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Submitted 18 January, 2019;
originally announced January 2019.