Safe landing is essential in robotics applications, from industrial settings to space exploration. As artificial intelligence advances, we have developed PEACE (Prompt Engineering Automation for CLIPSeg Enhancement), a system that automatically generates and refines prompts for identifying landing zones in changing environments. Traditional approaches using fixed prompts for open-vocabulary models struggle with environmental changes and can lead to dangerous outcomes when conditions are not represented in the predefined prompts. PEACE addresses this limitation by dynamically adapting to shifting data distributions. Our key innovation is the dual segmentation of safe and unsafe landing zones, allowing the system to refine the results by removing unsafe areas from potential landing sites. Using only monocular cameras and image segmentation, PEACE can safely guide descent operations from 100 meters to altitudes as low as 20 meters. The testing shows that PEACE significantly outperforms the standard CLIP and CLIPSeg prompting methods, improving the successful identification of safe landing zones from 57% to 92%. We have also demonstrated enhanced performance when replacing CLIPSeg with FastSAM. The complete source code is available as an open-source software1.

Exploration in unknown and unstructured environments is a pivotal requirement for robotic applications. A robot’s exploration behavior can be inherently affected by the performance of its Simultaneous Localization and Mapping (SLAM) subsystem, although SLAM and exploration are generally studied separately. In this paper, we formulate exploration as an active mapping problem and extend it with semantic information. We introduce a novel active metric-semantic SLAM approach, leveraging recent research advances in information theory and spectral graph theory: we combine semantic mutual information and the connectivity metrics of the underlying pose graph of the SLAM subsystem. We use the resulting utility function to evaluate different trajectories to select the most favorable strategy during exploration. Exploration and SLAM metrics are analyzed in experiments. Running our algorithm on the Habitat dataset, we show that, while maintaining efficiency close to the state-of-the-art exploration methods, our approach effectively increases the performance of metric-semantic SLAM with a 21% reduction in average map error and a 9% improvement in average semantic classification accuracy.

We propose a novel real-time LiDAR intensity image-based simultaneous localization and mapping method, which addresses the geometry degeneracy problem in un-structured environments. Traditional LiDAR-based front-end odometry mostly relies on geometric features such as points, lines and planes. A lack of these features in the environment can lead to the failure of the entire odometry system. To avoid this problem, we extract feature points from the LiDAR-generated point cloud that match features identified in LiDAR intensity images. We then use the extracted feature points to perform scan registration and estimate the robot ego-movement. For the back-end, we jointly optimize the distance between the corresponding feature points, and the point to plane distance for planes identified in the map. In addition, we use the features extracted from intensity images to detect loop closure candidates from previous scans and perform pose graph optimization. Our experiments show that our method can run in real time with high accuracy and works well with illumination changes, low-texture, and unstructured environments.

The topology of a robotic swarm affects the convergence speed of consensus and the mobility of the robots. In this paper, we prove the existence of a complete set of local topology manipulation operations that allow the transformation of a swarm topology. The set is complete in the sense that any other possible set of manipulation operations can be performed by a sequence of operations from our set. The operations are local as they depend only on the first and second hop neighbors' information to transform any initial spanning tree of the network's graph to any other connected tree with the same number of nodes. The flexibility provided by our method is similar to global methods that require full knowledge of the swarm network. We prove the existence of a sequence of transformations for any tree-to-tree transformation, and derive sequences of operations to form a line or star from any initial spanning tree. Our work provides a theoretical and practical framework for topological control of a swarm, establishing global properties using only local information.

This paper provides an overview of the latest trends in robotics research and development, with a particular focus on applications in manufacturing and industrial settings. We highlight recent advances in robot design, including cutting-edge collaborative robot mechanics and advanced safety features, as well as exciting developments in perception and human-swarm interaction. By examining recent contributions from Kinova, a leading robotics company, we illustrate the differences between industry and academia in their approaches to developing innovative robotic systems and technologies that enhance productivity and safety in the workplace. Ultimately, this paper demonstrates the tremendous potential of robotics to revolutionize manufacturing and industrial operations, and underscores the crucial role of companies like Kinova in driving this transformation forward.

Event-based cameras (ECs) are bio-inspired sensors that asynchronously report pixel brightness changes. Due to their high dynamic range, pixel bandwidth, temporal resolution, low power consumption, and computational simplicity, they are beneficial for vision-based projects in challenging lighting conditions and they can detect fast movements with their microsecond response time. The first generation of ECs are monochrome, but color data is very useful and sometimes essential for certain vision-based applications. The latest technology enables manufacturers to build color ECs, trading off the size of the sensor and substantially reducing the resolution compared to monochrome models, despite having the same bandwidth. In addition, ECs only detect changes in light and do not show static or slowly moving objects. We introduce a method to detect full RGB events using a monochrome EC aided by a structured light projector. The projector emits rapidly changing RGB patterns of light beams on the scene, the reflection of which is captured by the EC. We combine the benefits of ECs and projection-based techniques and allow depth and color detection of static or moving objects with a commercial TI LightCrafter 4500 projector and a monocular monochrome EC, paving the way for frameless RGBD sensing applications. Our code is available publicly: github.com/MISTLab/event_based_rgbd_ros

Flying robots are usually rather delicate and require protective enclosures when facing the risk of collision, while high complexity and reduced payload are recurrent problems with collision-resilient flying robots. Inspired by arthropods' exoskeletons, we design a simple, open source, easily manufactured, semi-rigid structure with soft joints that can withstand high-velocity impacts. With an exoskeleton, the protective shell becomes part of the main robot structure, thereby minimizing its loss in payload capacity. Our design is simple to build and customize using cheap components (e.g. bamboo skewers) and consumer-grade 3D printers. The result is CogniFly, a sub-250 g autonomous quadcopter that survives multiple collisions at speeds up to 7 m s−1. In addition to its collision-resilience, CogniFly carries sensors that allow it to fly for approx. 17 min without the need of GPS or an external motion capture system, and it has enough computing power to run deep neural network models on-board. This structure becomes an ideal platform for high-risk activities, such as flying in a cluttered environment or reinforcement learning training, by dramatically reducing the risks of damaging its own hardware or the environment. Source code, 3D files, instructions and videos are available (open source license) through the project's website: https://thecognifly.github.io.

Exploration of unknown environments is an important challenge in the field of robotics. While a single robot can achieve this task alone, evidence suggests it could be accomplished more efficiently by groups of robots, with advantages in terms of terrain coverage as well as robustness to failures. Exploration can be guided through belief maps, which provide probabilistic information about which part of the terrain is interesting to explore (either based on risk management or reward). This process can be centrally coordinated by building a collective belief map on a common server. However, relying on a central processing station creates a communication bottleneck and single point of failure for the system. In this paper, we present Distributed Online Risk-Aware (DORA) Explorer, an exploration system that leverages decentralized information sharing to update a common risk belief map. DORA-Explorer allows a group of robots to explore an unknown environment discretized as a 2D grid with obstacles, with high coverage while minimizing exposure to risk, effectively reducing robot failures.

The use of multi-robot systems is increasing in disaster response, industry, transport, and logistics. Humans will remain indispensable to control and manage these fleets of robots, particularly in safety-critical applications. However, a human operator’s cognitive capacities can be challenged and exceeded as the sizes of autonomous fleets grow, and more sophisticated AI techniques can lead to opaque robot control programs. In a user study (n = 40), we explore how autonomous swarm intelligence algorithms and novel tangible interaction modalities relate to subjective and physiological indices of operator cognitive load (i.e., NASA Task Load Index, heart rate variability). Our findings suggest that there are differences in workload across conditions; however, subjective and cardiac measures appear to be sensitive to different aspects of cognitive state. The results hint at the potential of both tangible interfaces and automation to engage operators and reduce cognitive load, yet show the need for further validation of workload measures for use in studying and optimizing human-swarm interactions.

One of the key factors for extended autonomy and resilience of battery-powered multirobot systems is their ability to maintain energy sufficiency by recharging when needed. In situations with limited access to charging facilities, robots need to be able to share and coordinate recharging activities, with guarantees that no robot will run out of energy. In this work, we present an approach based on control barrier functions (CBFs) to enforce both energy sufficiency (ensuring that no robot runs out of battery) and coordination constraints (guaranteeing mutual exclusive use of an available charging station) in a mission agnostic fashion. Moreover, we investigate the system capacity in terms of the relation between individual robot properties and the limit on temporal separation requirements within charging cycles. We show physics-based simulation results as well as real robot experiments that demonstrate the effectiveness of the proposed approach.

Connectivity maintenance is crucial for the real world deployment of multi-robot systems, as it ultimately allows the robots to communicate, coordinate and perform tasks in a collaborative way. A connectivity maintenance controller must keep the multi-robot system connected independently from the system’s mission and in the presence of undesired real world effects such as communication delays, model errors, and computational time delays, among others. In this paper we present the implementation, on a real robotic setup, of a connectivity maintenance control strategy based on Control Barrier Functions. During experimentation, we found that the presence of communication delays has a significant impact on the performance of the controlled system, with respect to the ideal case. We propose a heuristic to counteract the effects of communication delays, and we verify its efficacy both in simulation and with physical robot experiments.

Many applications related to the Internet of Things, such as tracking people or objects, robotics, and monitoring require the localization of large networks of devices in dynamic, GPS-denied environments. Ultrawideband (UWB) technology is a common choice because of its precise ranging capability. However, allowing access and effective use of the shared UWB medium with a constantly changing set of devices faces some particular challenges: high frequency of ranging measurements by the devices to improve system accuracy; network topology changes requiring rapid adaptation; and decentralized operation to avoid single points of failure. In this article, we propose a novel time-division multiple access (TDMA) algorithm that can quickly schedule the use of the UWB medium by a large network of devices without collisions in local network neighborhoods and avoiding conflicts with hidden terminals, all the while maximizing network usage. Using exclusively the UWB radio network, we realize a decentralized system for synchronization, dynamic TDMA scheduling, and precise relative positioning on a multihop network. Our system does not have special nodes (all nodes are equal) and it is sufficiently scalable for real-world applications. Our method can be applied to implement device localization services in large spaces without GPS and complex topologies, such as malls, museums, mines, etc. We demonstrate our method in simulation and on real hardware in an underground parking lot, showing the effectiveness of its TDMA schedule for relative localization.

Accurate localization and tracking are a fundamental requirement for robotic applications. Localization systems like GPS, optical tracking, simultaneous localization and mapping (SLAM) are used for daily life activities, research, and commercial applications. Ultra-wideband (UWB) technology provides another venue to accurately locate devices both indoors and outdoors. In this paper, we study a localization solution with a single UWB anchor, instead of the traditional multi-anchor setup. Besides the challenge of a single UWB ranging source, the only other sensor we require is a low-cost 9 DoF inertial measurement unit (IMU). Under such a configuration, we propose continuous monitoring of UWB range changes to estimate the robot speed when moving on a line. Combining speed estimation with orientation estimation from the IMU sensor, the system becomes temporally observable. We use an Extended Kalman Filter (EKF) to estimate the pose of a robot. With our solution, we can effectively correct the accumulated error and maintain accurate tracking of a moving robot.

Using multiple robots for exploring and mapping environments can provide improved robustness and performance, but it can be difficult to implement. In particular, limited communication bandwidth is a considerable constraint when a robot needs to determine if it has visited a location that was previously explored by another robot, as it requires for robots to share descriptions of places they have visited. One way to compress this description is to use constellations, groups of 3D points that correspond to the estimate of a set of relative object positions. Constellations maintain the same pattern from different viewpoints and can be robust to illumination changes or dynamic elements. We present a method to extract from these constellations compact spatial and semantic descriptors of the objects in a scene. We use this representation in a 2step decentralized loop closure verification: first, we distribute the compact semantic descriptors to determine which other robots might have seen scenes with similar objects; then we query matching robots with the full constellation to validate the match using geometric information. The proposed method requires less memory, is more interpretable than global image descriptors, and could be useful for other tasks and interactions with the environment. We validate our system's performance on a TUM RGB-D SLAM sequence and show its benefits in terms of bandwidth requirements.

One of the key factors for extended autonomy and resilience of multi-robot systems, especially when robots operate on batteries, is their ability to maintain energy sufficiency by recharging when needed. In situations with limited access to charging facilities, robots need to be able to share and coordinate recharging activities, with guarantees that no robot will run out of energy. In this work, we present an approach based on Control Barrier Functions (CBFs) to enforce both energy sufficiency (assuring that no robot runs out of battery) and coordination constraints (guaranteeing mutual exclusive use of an available charging station), all in a mission agnostic fashion. Moreover, we investigate the system capacity in terms of the relation between feasible requirements of charging cycles and individual robot properties. We show simulation results, using a physics-based simulator and real robot experiments to demonstrate the effectiveness of the proposed approach.

Small unmanned aerial vehicles (UAVs) have generally little capacity to carry payloads. Through collaboration, the UAVs can increase their joint payload capacity and carry more significant loads. For maximum flexibility to dynamic and unstructured environments and task demands, we propose a fully decentralized control infrastructure based on a swarm-specific scripting language, Buzz. In this paper, we describe the control infrastructure and use it to compare two algorithms for collaborative transport: field potentials and spring-damper. We test the performance of our approach with a fleet of micro-UAVs, demonstrating the potential of decentralized control for collaborative transport.

Robot swarms herald the ability to solve complex tasks using a large collection of simple devices. However, engineering a robotic swarm is far from trivial, with a major hurdle being the definition of the control laws leading to the desired globally coordinated behavior. Communication is a key element for coordination and it is considered one of the current most important challenges for swarm robotics. In this paper, we study the problem of maintaining robust swarm connectivity while performing a coverage task based on the Voronoi tessellation of an area of interest. We implement our methodology in a team of eight Khepera IV robots. With the assumptions that robots have a limited sensing and communication range-and cannot rely on centralized processing-we propose a tri-objective control law that outperforms other simpler strategies (e.g. a potential-based coverage) in terms of network connectivity, robustness to failure, and area coverage.

The control of multiple robots in the context of tele-exploration tasks is often attentionally taxing, resulting in a loss of situational awareness for operators. Unmanned aerial vehicle swarms require significantly more multitasking than controlling a plane, thus making it necessary to devise intuitive feedback sources and control methods for these robots. The purpose of this article is to examine a swarm's nonverbal behaviour as a possible way to increase situational awareness and reduce the operators cognitive load by soliciting intuitions about the swarm's behaviour. To progress on the definition of a database of nonverbal expressions for robot swarms, we first define categories of communicative intents based on spontaneous descriptions of common swarm behaviours. The obtained typology confirms that the first two levels (as defined by Endsley: elements of environment and comprehension of the situation) can be shared through swarms motion-based communication. We then investigate group motion parameters potentially connected to these communicative intents. Results are that synchronized movement and tendency to form figures help convey meaningful information to the operator. We then discuss how this can be applied to realistic scenarios for the intuitive command of remote robotic teams.

In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

Swarm robotics carries the potential of solving complex tasks using simple devices. To do so, however, one must define distributed control algorithms capable of producing globally coordinated behaviours. We propose a methodology to address the problem of the spatial coverage of multiple tasks with a swarm of robots that must not lose global connectivity. Our methodology comprises two layers: (i) a distributed Robot Navigation Controller (RNC) is responsible for simultaneously guaranteeing connectivity and pursuit of multiple tasks; and (ii) a global Task Scheduling Controller approximates the optimal strategy for the RNC with minimal computational load. Our contributions include: (i) a qualitative analysis of the literature on connectivity assessment, (ii) our proposed methodology, (iii) simulations in a multi-physics environment, (iv) real-life robot experiments, and (v) the experimental validation of connectivity, coverage optimality, and fault-tolerance.

We present a decentralized and scalable approach for deployment of a robot swarm. Our approach tackles scenarios in which the swarm must reach multiple spatially distributed targets, and enforce the constraint that the robot network cannot be split. The basic idea behind our work is to construct a logical tree topology over the physical network formed by the robots. The logical tree acts as a backbone used by robots to enforce connectivity constraints. We study and compare two algorithms to form the logical tree: outwards and inwards. These algorithms differ in the order in which the robots join the tree: the outwards algorithm starts at the tree root and grows towards the targets, while the inwards algorithm proceeds in the opposite manner. Both algorithms perform periodic reconfiguration, to prevent suboptimal topologies from halting the growth of the tree. Our contributions are (i) The formulation of the two algorithms; (ii) A comparison of the algorithms in extensive physics-based simulations; (iii) A validation of our findings through real-robot experiments.

One of the safest and most reliable strategies for vehicle's collision avoidance is embedded control at low level to guarantee safe motion in all situations using on-board sensors. In this paper, we propose a novel lightweight collision avoidance strategy that can be implemented as a low level motion control to achieve safe motion while simultaneously tracking the robot's reference control input. This strategy is designed to be general so that it can be easily integrated with most control designs, with the primary target of resource-constrained robot swarms that act in real-time, dynamic environments. The main advantages of our approach are a very simple structure and low computational requirements. We verified the effectiveness of the proposed collision avoidance strategy through two simulated scenarios and with physical robots. We believe our design can be directly used in many areas, such as autonomous driving, intelligent transportation and planetary exploration.

In this paper, we demonstrate how behavioural structure, such as a finite state machine, can emerge in minimal robots without computation nor memory capabilities. As a case study we observe the ability of a group of non-holonomic robots to form robust, self-healing circle formations in a decentralized manner using only a limited frontal binary sensor. We present a grid-search method to find suitable parameters that promote the formation of a stable circle. We then examine how the parameters of the controllers affect the appearance of the behaviour, and provide theoretical proof for its emergence and self-healing properties. We validate the proposed model through a set of experiments with ten mobile real robots. Our results with real robots match the simulated experiments and provide insights on how a simple, computation-free behaviour can generate complex spatio-temporal dynamics.

The robotics community is considering the use of large groups of robots, also known as artificial swarms for applications in unknown and dynamic environments. In this context, swarms of robot will need to interact with users to accomplish their mission. Unfortunately, little is known about the users' perception of group behavior and dynamics, as well as what is the best interaction modality for swarms. In this paper, we focus on the movement of the swarm as a group to convey information to a user: we believe that the interpretation of artificial states based solely on the motion can lead to promising natural interaction modalities. We define the expressivity of a movement as a metric to understand how natural, readable, or easily understandable such movement may appear. We then correlate expressivity with the control parameters for the distributed behaviour of the swarm. A user study confirms the relationship between inter-robot distance, temporal and spatial synchronicity, and the perceived expressivity of the robotic system.

The effect of sharing the last-level cache (LLC) among cores in a multi-core system has not been thoroughly investigated especially in the design of efficient scheduling algorithms. And with the growing interest in random caches, which allow for an easier estimation of the worst-case execution time of tasks in critical real-time embedded systems, tools that analyse the sensitivity of workloads to sharing the LLC become necessary. In this paper, we extend a realtime multiprocessor scheduling simulator, SimSo, with a framework that incorporates a random cache model for multi-level caches to evaluate emerging scheduling algorithms under the influence of shared caches. A set of experiments were performed to study the behavior of workloads with respect to worst-case response time, average slack time, and maximum utilization, with varying cache designs under different scheduling algorithms.

Accurate timing prediction for real-time embedded software execution is becoming a problem due to the increasing complexity of computer architecture, and the presence of mixed-criticality workloads. Probabilistic caches were proposed to set bounds to Worst Case Execution Time (WCET) estimates and help designers improve real-time embedded system resource use. Static Probabilistic Timing Analysis (SPTA) for probabilistic caches is nevertheless difficult to perform, because cache accesses depend on execution history, and the computational complexity of SPTA makes it intractable for calculation as the number of accesses increases. In this paper, we explore and improve SPTA for caches with evict-on-miss random replacement policy using a state space modeling technique. A nonhomogeneous Markov model is employed for single-path programs in discrete-time finite state space representation. To make this Markov model tractable, we limit the number of states and use an adaptive method for state modification. Experiments show that compared to the state-of-the-art methodology, the proposed adaptive Markov chain approach provides better results at the occurrence probability of 10 −15 : in terms of accuracy, the state-of-the-art SPTA results are more conservative, by 11% more on average. In terms of computation time, our approach is not significantly different from the state-of-the-art SPTA.

We present Buzz, a novel programming language for heterogeneous robot swarms. Buzz advocates a compositional approach, offering primitives to define swarm behaviors both from the perspective of the single robot and of the overall swarm. Single-robot primitives include robot-specific instructions and manipulation of neighborhood data. Swarm-based primitives allow for the dynamic management of robot teams, and for sharing information globally across the swarm. Self-organization stems from the completely decentralized mechanisms upon which the Buzz run-time platform is based. The language can be extended to add new primitives (thus supporting heterogeneous robot swarms), and its run-time platform is designed to be laid on top of other frameworks, such as the Robot Operating System. We showcase the capabilities of Buzz by providing code examples, and analyze scalability and robustness of the run-time platform through realistic simulated experiments with representative swarm algorithms.

Efficient mapping of tasks onto heterogeneous multi-core systems is very challenging especially under hard timing constraints. Assigning tasks to processors is an NP-hard problem and solving it requires the use of meta-heuristics. Relevantly, genetic algorithms have already proven to be one of the most powerful and widely used stochastic tools to solve this problem. Conventional genetic algorithms were initially defined as a general evolutionary algorithm based on blind operators. It is commonly admitted that the use of these operators is quite poor for an efficient exploration. Like-wise, since exhaustive exploration of the solution space is unrealistic, a potent option is often to guide the exploration process by hints, derived by problem structure. This guided exploration prioritizes fitter solutions to be part of next generations and avoids exploring unpromising configurations by transmitting a set of predefined criteria from parents to children. Consequently, genetic operators, such as crossover, must incorporate specific domain knowledge to intelligently guide the exploration of the solution space. In this paper, we illustrate and evaluate the impact of crossover operators and we propose a hybrid genetic algorithm based on a novel schedulability-guided operator that easily outperforms the classical operators by offering at least 21% improvement in terms of ratio of certainly schedulable tasks.

In this paper, we present a recommendation system aimed at helping users and developers alike. We help users to choose optimal sets of applications belonging to different categories (eg. browsers, e-mails, cameras) while minimizing energy consumption, transmitted data, and maximizing application rating. We also help developers by showing the relative placement of their application's efficiency with respect to selected others. When the optimal set of applications is computed, it is leveraged to position a given application with respect to the optimal, median and worst application in its category (eg. browsers). Out of eight categories we selected 144 applications, manually defined typical execution scenarios, collected the relevant data, and computed the Pareto optimal front solving a multi-objective optimization problem. We report evidence that, on the one hand, ratings do not correlate with energy efficiency and data frugality. On the other hand, we show that it is possible to help developers understanding how far is a new Android application power consumption and network usage with respect to optimal applications in the same category. From the user perspective, we show that choosing optimal sets of applications, power consumption and network usage can be reduced by 16.61% and 40.17%, respectively, in comparison to choosing the set of applications that maximizes only the rating.

Three-dimensional integrated circuits (3D ICs) with advanced cooling systems are emerging as a viable solution for many-core platforms. These architectures generate a high and rapidly changing thermal flux. Their design requires accurate transient thermal models. Several models have been proposed, either with limited capabilities, or poor simulation performance. This work introduces an efficient algorithm based on the Finite Difference Method to compute the transient temperature in liquid-cooled 3D ICs. Our experiments show a 5x speedup versus state-of-the-art models, while maintaining the same level of accuracy, and demonstrate the effect of large through silicon vias arrays on thermal dissipation.

In this paper, we propose a parameterized crypto co-processor based on Advanced Encryption Standard (AES). This parameterized AES module is combined with a 32-bit general purpose 5-stage pipelined MIPS processor. The AES module used in this paper is fully pipelined. The processor fetches an instruction from the instruction memory and sends it to the decode stage. If the instruction is the crypto instruction it is pushed into the AES module during the decode stage. However if the instruction belongs to the MIPS processor, the remaining cycles will be completed on the MIPS processor. The parameterized AES module has different latencies on different rounds of AES according to the application requirements. The effects of different number of rounds on latency, memory, and area are studied and reported.

This article presents a detailed overview and the experimental comparison of 15 multi-objective design-space exploration (DSE) algorithms for high-level design. These algorithms are collected from recent literature and include heuristic, evolutionary, and statistical methods. To provide a fair comparison, the algorithms are classified according to the approach used and examined against a large set of metrics. In particular, the effectiveness of each algorithm was evaluated for the optimization of a multiprocessor platform, considering initial setup effort, rate of convergence, scalability, and quality of the resulting optimization. Our experiments are performed with statistical rigor, using a set of very diverse benchmark applications (a video converter, a parallel compression algorithm, and a fast Fourier transformation algorithm) to take a large spectrum of realistic workloads into account. Our results provide insights on the effort required to apply each algorithm to a target design space, the number of simulations it requires, its accuracy, and its precision. These insights are used to draw guidelines for the choice of DSE algorithms according to the type and size of design space to be optimized.

The high heat flux and compact structure of three-dimensional circuits (3D ICs) make conventional air-cooled devices more subsceptible to overheating. Liquid cooling is an alternative that can improve heat dissipation, and reduce thermal issues. Fast and accurate thermal models are needed to appropriately dimension the cooling system at design time. Several models have been proposed to study different designs, but generally with low simulation performance. In this paper, we present an efficient model of the transient thermal behaviour of liquid-cooled 3D ICs. In our experiments, our approach is 60 times faster and uses 600 times less memory than state-of-the-art models, while maintaining the same level of accuracy.

Verification has become one of the main bottlenecks in the design process of embedded systems, particularly for Multiprocessor Systems-on-Chip (MPSoCs). Efficiently proving the correctness of a design is of extreme importance to reduce cost and time-to-market. Simulation is a common verification method, but complex systems usually require long simulation times. This work advocates Constraint Programming (CP) as a powerful tool for the verification of performance metrics of MPSoCs. Our methodology was evaluated using streaming applications mapped onto a target MPSoC. The resulting constraint-based scheduling problem allowed us to identify performance constraint violations in a fraction of the time required by simulation-based verification.

This paper presents an efficient technique to perform multi-objective design space exploration of a multiprocessor platform. Instead of using semi-random search algorithms (like simulated annealing, tabu search, genetic algorithms, etc.), we use the domain knowledge derived from the platform architecture to set-up the exploration as a discrete-space multi-objective Markov Decision Process (MDP). The system walks the design space changing its parameters, performing simulations only when probabilistic information becomes insufficient for a decision. The algorithm employs a novel multi-objective value function and exploration strategy, which guarantees high accuracy and minimizes the number of necessary simulations. The proposed technique has been tested with a small benchmark (to compare the results against exhaustive exploration) and two large applications (to prove effectiveness in a real case), namely the ffmpeg transcoder and pigz parallel compressor. Results show that the exploration can be performed with 10% of the simulations necessary for state-of-the-art exploration algorithms and with unrivaled accuracy (0.6 ± 0.05% error).

Three-dimensional (3D) integrated circuits (IC) are emerging as a viable solution to enhance the performance of Multi-processor System-On-Chip (MPSoC). The use of highspeed hardware and the increased density of 3D architectures present novel challenges concerning thermal dissipation and power management. Most approaches at power and thermal modeling use either static analytical models or slow low-level analog simulations. In this paper, we propose a novel thermal modeling methodology for evaluation of 3D MPSoCs. The integration of this methodology in a virtual platform enables effcient dynamic thermal evaluation of a chip. We present initial results for an architecture based on a 3D Network-On-Chip (NoC) interconnecting 2D processing elements (PE). Our methodology is based on the finite difference method: we perform an initial static characterization, after which high-speed dynamic simulation is possible.

This paper presents an efficient technique to perform design space exploration of a multiprocessor platform that minimizes the number of simulations needed to identify a Pareto curve with metrics like energy and delay. Instead of using semi-random search algorithms (like simulated annealing, tabu search, genetic algorithms, etc.), we use the domain knowledge derived from the platform architecture to set-up the exploration as a discrete-spaceMarkov decision process. The system walks the design space changing its parameters, performing simulations only when probabilistic information becomes insufficient for a decision. A learning algorithm updates the probabilities of decision outcomes as simulations are performed. The proposed technique has been tested with two multimedia industrial applications, namely the ffmpeg transcoder and the parallel pigz compression algorithm. Results show that the exploration can be performed with 5% of the simulations necessary for the most used algorithms (Pareto simulated annealing, nondominated sorting genetic algorithm, etc.), increasing the exploration speed by more than one order of magnitude.

This paper presents a novel technique for the modeling, simulation, and analysis of real-time applications on Multi-Processor Systems-on-Chip (MPSoCs). This technique is based on an application-transparent emulation of OS primitives, including support for RTOS elements. The proposed methodology enables a quick evaluation of the real-time performance of an application in front of different design choices, including the study of system's behavior as tasks' deadlines become stricter or looser. The approach has been verified on a large set of multi-threaded benchmarks. Results show that our methodology (a) enables accurate realtime and responsiveness analysis of parallel applications running on MPSOCs, (b) allows the designer to devise an optimal interrupt distribution mechanism for the given application, and (c) helps dimensioning the system to meet performance and real-time needs.

Fault modeling is a fundamental element for several activities, ranging from off- and on-line testing, to fault tolerance and dependability-aware design. These activities are carried out during various design phases, dealing with specifications at different abstraction levels. Therefore, modeling faults across abstraction levels is of paramount importance to introduce dependability-related issues from the early phases of design. This paper analyzes how faults can be modeled at the different levels of abstraction with respect to Transaction Level Models, and how these models are related across levels. The work focuses on soft errors and aims at providing support to dependability analysis. A case study of a Transaction Level specification of a Network-on-Chip switch is used to evaluate the methodology and its applicability.

This paper presents reflective simulation platform (ReSP), a transaction-level multiprocessor simulation platform based on the integration of SystemC and Python. ReSP exploits the concept of reflection, enabling the integration of SystemC components without source-code modifications and providing full observability of their internal state. ReSP offers fine-grained simulation control and supports the evaluation of different hardware/software configurations of a given application, enabling complete design space exploration. ReSP allows the evaluation of real-time applications on high-level hardware models since it provides the transparent emulation of POSIX-compliant real-time operating systems (RTOS) primitives. A number of experiments have been performed to validate ReSP and its capabilities, using a set of single- and multithreaded benchmarks, with both POSIX Threads (PThreads) and OpenMP programming styles. These experiments confirm that reflection introduces negligible ( <1%) overhead when comparing ReSP to plain SystemC simulation. The results also show that ReSP can be successfully used to analyze and explore concurrent and reconfigurable applications even at very early development stages. In fact, the average error introduced by ReSP's RTOS emulation is below 6.6 plusmn 5% w.r.t. the same RTOS running on an instruction set simulator, while simulation speed increases by a factor of ten. Owing to the integration with a scripted language, simulation management is simplified, and experimental setup effort is considerably reduced.

This paper presents ReSP (Reflective Simulation Platform), a Transaction-Level multi-processor simulation platform based on SystemC and Python; SystemC is a standard language for system modeling and verification, and Python provides the platform with reflective capabilities. These are employed to give the designer an easy way to specify the architecture of a system, simulate the given configuration and perform automatic analysis on it. ReSP enables SystemC and Python interoperability through automatic Python wrapper generation. We show that the overhead associated with the Python intermediate layer is around 1%, therefore execution speed is not compromised. The advantages of our approach are: (a) easy integration of external IPs (b) fine grain control of the simulation (c) effortless integration of tools for system analysis and design space exploration. A case study shows how the platform can be extended to support system reliability assessment.

This paper introduces a modeling and simulation technique that extends transaction-level modeling (TLM) to support multi-accuracy models and power estimation. This approach provides different combinations of power and performance models, and the switching of model accuracy during simulation, allowing the designer to trade off between simulation accuracy and speed at runtime. This is particularly useful during the exploration phase of a design, when the designer changes the features or the parameters of the design, trying to satisfy its constraints. Usually, only limited portions of a system are affected by a single parameter change, and therefore, it is possible to fast-simulate uninteresting sections of the application. In particular, we show how to extend the TLM and modify the SystemC kernel to support multi-accuracy features. The proposed methodology has been tested on several benchmarks, among which is an MPEG4 encoder, showing that simulation speed can be increased of one order of magnitude. On the same benchmarks, we also show how it is possible to choose the optimal performance simulation accuracy for a given power model, maximizing simulation speed for the desired accuracy.

The introduction of transaction level modeling (TLM) allows a system designer to model a complete application, composed of hardware and software parts, at several levels of abstraction. The simulation speed of TLM is orders of magnitude faster than traditional RTL simulation; nevertheless, it can become a limiting factor when considering a multi-processor system-on-chip (MP-SoC), as the analysis of these systems can be very complex. The main goal of this paper is to introduce a novel way of exploiting TLM features to increase simulation efficiency of complex systems by switching TLM models at runtime. Results show that simulation performance can be increased significantly without sacrificing the accuracy of critical application kernels

This paper introduces an application mapping methodology and case study for multiprocessor on-chip architectures. Starting from the description of an application in standard sequential code (e.g. in C), first the application is profiled, parallelized when possible, and then its components are moved to hardware implementation when necessary to satisfy performance and power constraints. The key contribution of this work is a methodology for high-level hardware/software partitioning that allows the designer to use the same code for both hardware and software models for simulation, providing nevertheless preliminary estimations for timing and power consumption. The methodology has been applied to the co-exploration of an industrial case study: an MPEG4 VGA realtime encoder

The MultiFlex system is an application-to-platform mapping tool that integrates heterogeneous parallel components-H/W or S/W- into a homogeneous platform programming environment. This leads to higher quality designs through encapsulation and abstraction. Two high-level parallel programming models are supported by the following MultiFlex platform mapping tools: a distributed system object component (DSOC) object-oriented message passing model and a symmetrical multiprocessing (SMP) model using shared memory. We demonstrate the combined use of the MultiFlex multiprocessor mapping tools, supported by high-speed hardware-assisted messaging, context-switching, and dynamic scheduling using the StepNP demonstrator multiprocessor system-on-chip platform, for two representative applications: 1) an Internet traffic management application running at 2.5 Gb/s and 2) an MPEG4 video encoder (VGA resolution, at 30 frames/s). For these applications, a combination of the DSOC and SMP programming models were used in interoperable fashion. After optimization and mapping, processor utilization rates of 85%-91% were demonstrated for the traffic manager. For the MPEG4 decoder, the average processor utilization was 88%

In this paper, we propose a power/performance estimation layer designed for StepNP, a system-level architecture simulation and exploration platform for Network Processors and Multi-Processor Systems-on-Chip (MP-SoCs). The first goal of our work is to plug-in PIRATE, a parameterizable Network on-Chip in the StepNP platform, to support a fast exploration of on-chip interconnection networks. Up to now, StepNP does not provide any energy profiling, so our second goal is to dynamically plug-in power models of the different system components to provide power estimates quickly. The proposed power/performance exploration framework is based on a power characterization methodology and a system-level simulator to dynamically profile the given network application. This framework is intended to be used at different levels of the design, considering several levels of accuracy and taking full advantage of the StepNP performance profiling features. Experimental results are provided for the exploration of an ARM-based MP-SOC including a configurable NoC-IP executing an IPv4 forwarding application.

The aim of this work is to provide an elegant and accurate static execution timing model for 32-bit microprocessor instruction sets, covering also inter-instruction effects. Such effects depend on the processor state and the pipeline behavior, and are related to the dynamic execution of assembly code. The paper proposes a mathematical model of the delays deriving from instruction dependencies and gives a statistical characterization of such timing overheads. The model has been validated on a commercial architecture, the Intel486, by means of timing analysis of a set of benchmarks, obtaining an error within 5%. This model can be seamlessly integrated with a static energy consumption model in order to obtain precise software power and energy estimations.