In this article, we present a novel logic design style, namely, memristor overwrite logic (MOL), associated with an original MOL-based computational memory. MOL relies on a fully digital representation of memristor and can operate with different memristive device technologies. Its integration in memristive crossbar arrays and computational memories allows the execution of bit and vector-level primitive logic operations in two computational steps at most. Promising features and performances are demonstrated through the implementation of N-bit full addition using the proposed MOL-based computational memory.

A great deal of effort has recently been devoted to extending the usage of memristor technology from memory to computing. Memristor-based logic design is an emerging concept that targets efficient computing systems. Several logic families have evolved, each with different attributes. Memristor Ratioed Logic (MRL) has been recently introduced as a hybrid memristor–CMOS logic family. MRL requires an efficient design strategy that takes into consideration the implementation phase. This paper presents a novel MRL-based crossbar design: X-MRL. The proposed structure combines the density and scalability attributes of memristive crossbar arrays and the opportunity of their implementation at the top of CMOS layer. The evaluation of the proposed approach is performed through the design of an X-MRL-based full adder. The design is presented with its layout and corresponding simulation results using the Cadence Virtuoso toolset and CMOS $65$nm process. The comparison with a pure CMOS implementation is promising in terms of the area, as our approach exhibits a 44.79% area reduction. Moreover, the combined Energy.Delay metric demonstrates a significant improvement (between $\times 5.7$ and $\times 31$) with respect to the available literature.

This paper addresses the challenge of embedded computing resources required by future autonomous Unmanned Aircraft Systems (UAS). Based on an analysis of the required onboard functions that will lead to higher levels of autonomy, we look at most common UAS tasks to first propose a classification of UAS tasks considering categories such as flight, navigation, safety, mission and executing entities such as human, offline machine, embedded system. We then analyse how a given combination of tasks can lead to higher levels of autonomy by defining an autonomy level. We link UAS applications, the tasks required by those applications, the autonomy level and the implications on computing resources to achieve that autonomy level. We provide insights on how to define a given autonomy level for a given application based on a number of tasks. Our study relies on the state-of-the-art hardware and software implementations of the most common tasks currently used by UAS, also expected tasks according to the nature of their future missions. We conclude that current computing architectures are unlikely to meet the autonomy requirements of future UAS. Our proposed approach is based on dynamically reconfigurable hardware that offers benefits in computational performance and energy usage. We believe that UAS designers must now consider the embedded system as a masterpiece of the system.

This paper presents a scalable approach to model uncertainties within a UAV (Unmanned Aerial Vehicle) embedded mission manager. It proposes a concurrent version of BFM models, which are Bayesian Networks built from FMEA (Failure Mode and Effects Analysis) and used by MDPs (Markov Decision Processes). The models can separately handle different applications during the mission; they consider the context of the mission including external constraints (luminosity, climate, etc.), the health of the UAV (Energy, Sensor) as well as the computing resource availability including CPU (Central Processing Unit) load, FPGA (Field Programmable Gate Array) use and timing performances. The proposed solution integrates the constraints into a mission specification by means of FMEA tables in order to facilitate their specifications by non-experts. Decision-making processes are elaborated following a “just enough” quality management by automatically providing adequate implementation of the embedded applications in order to achieve the mission goals, in the context given by the sensors and the on-board monitors. We illustrate the concurrent BFM approach with a case study of a typical tracking UAV mission. This case also considers a FPGA-SoC (FPGA-System on Chip) platform into consideration and demonstrates the benefits to tune the quality of the embedded applications according to the environmental context.

The ever-increasing demands for autonomy and precision have led to the development of heavily computational multi-robot system (MRS). However, numerous missions exclude the use of robotic cloud. Another solution is to use the robotic cluster to locally distribute the computational load. This complex distribution requires adaptability to come up with a dynamic and uncertain environment. Classical approaches are too limited to solve this problem, but recent advances in reinforcement learning and deep learning offer new opportunities. In this paper we propose a new Deep Q-Network (DQN) based approaches where the MRS learns to distribute tasks directly from experience. Since the problem complexity leads to a curse of dimensionality, we use two specific methods, a new branching architecture, called Branching Dueling Q-Network (BDQ), and our own optimized multi-agent solution and we compare them with classical Market-based approaches as well as with non-distributed and purely local solutions. Our study shows the relevancy of learning-based methods for task mapping and also highlight the BDQ architecture capacity to solve high dimensional state space problems. Note to Practitioners —A lot of applications in industry like area exploration and monitoring can be efficiently delegated to a group of small-size robots or autonomous vehicles with advantages like reliability and cost in respect of single-robot solutions. But autonomy requires high and increasing compute-intensive tasks such as computer-vision. On the other hand small robots have energy constraints, limited embedded computing capacities and usually restricted and/or unreliable communications that limit the use of cloud resources. An alternative solution to cope with this problem consists in sharing the computing resources of the group of robots. Previous work was a proof of concept limited to the parallelisation of a single specific task. In this paper we formalize a general method that allows the group of robots to learn on the field how to efficiently distribute tasks in order to optimize the execution time of a mission under energy constraint. We demonstrate the relevancy of our solution over market-based and non-distributed approaches by means of intensive simulations. This successful study is a necessary first step towards distribution and parallelisation of computation tasks over a robotic cluster. The next steps, not tested yet, will address hardware in the loop simulation and finally a real-life mission with a group of robots.