Alessandro G. Di Nuovo

Efficient design space exploration for application specific systems-on-a-chip

A reduction in the time-to-market has led to widespread use of pre-designed parametric architectural solutions known as system-on-a-chip (SoC) platforms. A system designer has to configure the platform in such a way as to optimize it for the execution of a specific application. Very frequently, however, the space of possible configurations that can be mapped onto a SoC platform is huge and the computational effort needed to evaluate a single system configuration can be very costly. In this paper we propose an approach which tackles the problem of design space exploration (DSE) in both of the fronts of the reduction of the number of system configurations to be simulated and the reduction of the time required to evaluate (i.e., simulate) a system configuration. More precisely, we propose the use of Multi-objective Evolutionary Algorithms as optimization technique and Fuzzy Systems for the estimation of the performance indexes to be optimized. The proposed approach is applied on a highly parameterized SoC platform based on a parameterized VLIW processor and a parameterized memory hierarchy for the optimization of performance and power dissipation. The approach is evaluated in terms of both accuracy and efficiency and compared with several established DSE approaches. The results obtained for a set of multimedia applications show an improvement in both accuracy and exploration time.

Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems

Multi-objective evolutionary algorithms (MOEAs) have received increasing interest in industry because they have proved to be powerful optimizers. Despite the great success achieved, however, MOEAs have also encountered many challenges in real-world applications. One of the main difficulties in applying MOEAs is the large number of fitness evaluations (objective calculations) that are often needed before an acceptable solution can be found. There are, in fact, several industrial situations in which fitness evaluations are computationally expensive and the time available is very short. In these applications efficient strategies to approximate the fitness function have to be adopted, looking for a trade-off between optimization performance and efficiency. This is the case in designing a complex embedded system, where it is necessary to define an optimal architecture in relation to certain performance indexes while respecting strict time-to-market constraints. This activity, known as design space exploration (DSE), is still a great challenge for the EDA (electronic design automation) community. One of the most important bottlenecks in the overall design flow of an embedded system is due to simulation. Simulation occurs at every phase of the design flow and is used to evaluate a system which is a candidate for implementation. In this paper we focus on system level design, proposing an extensive comparison of the state-of-the-art of MOEA approaches with an approach based on fuzzy approximation to speed up the evaluation of a candidate system configuration. The comparison is performed in a real case study: optimization of the performance and power dissipation of embedded architectures based on a Very Long Instruction Word (VLIW) microprocessor in a mobile multimedia application domain. The results of the comparison demonstrate that the fuzzy approach outperforms in terms of both performance and efficiency the state of the art in MOEA strategies applied to DSE of a parameterized embedded system.

Fuzzy decision making in embedded system design

The use of Application Specific Instruction-set Processors (ASIP) is a solution to the problem of increasing complexity in embedded systems design. One of the major challenges in ASIP design is Design Space Exploration (DSE), because of the heterogeneity of the objectives and parameters involved. Typically DSE is a multi- objective search problem, where performance, power, area, etc. are the different optimization criteria. The output of a DSE strategy is a set of candidate design solutions called a Pareto-optimal set. Choosing a solution for system implementation from the Pareto- optimal set can be a difficult task, generally because Pareto-optimal sets can be extremely large or even contain an infinite number of solutions. In this paper we propose a methodology to assist the decision-maker in analysis of the solutions to multi-objective problems. By means of fuzzy clustering techniques, it finds the reduced Pareto subset, which best represents all the Pareto solutions. This optimal subset will be used for further and more accurate (but slower) analysis. As a real application example we address the optimization of area, performance, and power of a VLIW-based embedded system.

2013 Special Issue: Autonomous learning in humanoid robotics through mental imagery

In this paper we focus on modeling autonomous learning to improve performance of a humanoid robot through a modular artificial neural networks architecture. A model of a neural controller is presented, which allows a humanoid robot iCub to autonomously improve its sensorimotor skills. This is achieved by endowing the neural controller with a secondary neural system that, by exploiting the sensorimotor skills already acquired by the robot, is able to generate additional imaginary examples that can be used by the controller itself to improve the performance through a simulated mental training. Results and analysis presented in the paper provide evidence of the viability of the approach proposed and help to clarify the rational behind the chosen model and its implementation.

Making fingers and words count in a cognitive robot.

Evidence from developmental as well as neuroscientific studies suggest that finger counting activity plays an important role in the acquisition of numerical skills in children. It has been claimed that this skill helps in building motor-based representations of number that continue to influence number processing well into adulthood, facilitating the emergence of number concepts from sensorimotor experience through a bottom-up process. The act of counting also involves the acquisition and use of a verbal number system of which number words are the basic building blocks. Using a Cognitive Developmental Robotics paradigm we present results of a modeling experiment on whether finger counting and the association of number words (or tags) to fingers, could serve to bootstrap the representation of number in a cognitive robot, enabling it to perform basic numerical operations such as addition. The cognitive architecture of the robot is based on artificial neural networks, which enable the robot to learn both sensorimotor skills (finger counting) and linguistic skills (using number words). The results obtained in our experiments show that learning the number words in sequence along with finger configurations helps the fast building of the initial representation of number in the robot. Number knowledge, is instead, not as efficiently developed when number words are learned out of sequence without finger counting. Furthermore, the internal representations of the finger configurations themselves, developed by the robot as a result of the experiments, sustain the execution of basic arithmetic operations, something consistent with evidence coming from developmental research with children. The model and experiments demonstrate the importance of sensorimotor skill learning in robots for the acquisition of abstract knowledge such as numbers.

Mental practice and verbal instructions execution: A cognitive robotics study

Understanding the tight relationship that exists between mental imagery and motor activities (i.e. how images in the mind can influence movements and motor skills) has become a topic of interest and is of particular importance in domains in which improving those skills is crucial for obtaining better performance, such as in sports and rehabilitation. In this paper, using an embodied cognition approach and a cognitive robotics platform, we introduce initial results of an ongoing study that explores the impact linguistic stimuli could have in processes of mental imagery practice and subsequent motor execution and performance. Results are presented to show that the robot used, is able to “imagine” or “mentally” recall and accurately execute movements learned in previous training phases, strictly on the basis of the verbal commands issued. Further tests show that data obtained with “imagination” could be used to simulate “mental training” processes such as those that have been employed with human subjects in sports training, in order to enhance precision in the performance of new tasks, through the association of different verbal commands.

Robots in education and care of children with developmental disabilities : a study on acceptance by experienced and future professionals

Research in the area of robotics has made available numerous possibilities for further innovation in the education of children, especially in the rehabilitation of those with learning difficulties and/or intellectual disabilities. Despite the scientific evidence, there is still a strong scepticism against the use of robots in the fields of education and care of people. Here we present a study on the acceptance of robots by experienced practitioners (specialized in the treatment of intellectual disabilities) and university students in psychology and education sciences (as future professionals). The aim is to examine the factors, through the Unified Theory of Acceptance and Use of Technology (UTAUT) model, that may influence the decision to use a robot as an instrument in the practice. The overall results confirm the applicability of the model in the context of education and care of children, and suggest a positive attitude towards the use of the robot. The comparison highlights some scepticism among the practitioners, who perceive the robot as an expensive and limited tool, while students show a positive perception and a significantly higher willingness to use the robot. From this experience, we formulate the hypothesis that robots may be accepted if more integrated with standard rehabilitation protocols in a way that benefits can outweigh the costs.

A web based multi-modal interface for elderly users of the robot-era multi-robot services

In this paper we present the design and technical implementation of a web based Multi-Modal User Interface (MMUI) tailored for elderly users of the robotic services developed by the EU FP7 Large-Scale Integration Project Robot-Era. The project partners are working to significantly enhance the performance and acceptability of technological services for ageing well by delivering a fully realized system based on the cooperation of multiple heterogeneous robots and with the support of an Ambient Assisted Living environment. To this end, elderly users were involved in the definition of the services and in the design of the hardware and software of the robotic platforms from the first stages of the development process and in real experimentation in two test sites. In particular, here we detail the interface software system for multi-modal elderly-robot interaction. The MMUI is designed to run on any device including touch-screen mobiles and tablets that are preferred by the elderly. This is obtained by integrating web based solutions with the Robot-Era middlewares and planner. Finally we present some preliminary results of ongoing experiments to show the successful evaluation of usability by potential users and to discuss the future directions to improve the proposed MMUI software system.

Special issue on artificial mental imagery in cognitive systems and robotics

The present special issue of Adaptive Behavior is focused on exploiting the concept of mental imagery and mental simulation as a fundamental cognitive capability, as applied to artificial cognitive systems and robotics. The special issue is motivated by the fact that the processes behind the human ability to create mental images have recently become an object of renewed interest in cognitive science and, in particular, their applications in the field of artificial cognitive systems. With the aim of providing a panorama of the current research activity on the topic, this special issue presents seven selected contributions considered to be representative of the state of the art in the field. In the section that follows, we give a short introduction on recent work on mental imagery in general, and in the field of artificial cognitive systems in particular, in order to help the reader to contextualize the topic. Subsequently, we summarize the new findings that this special issue presents. Mental imagery has long been the subject of research and debate in philosophy, psychology, cognitive science, and more recently, neuroscience (Kosslyn, 1996), but only quite recently a growing amount of evidence from empirical studies has begun to demonstrate the relationship between bodily experiences and mental processes that actively involve body representations. This is also due to the fact that, in the past, philosophical and scientific investigations of the topic primarily focused upon visual mental imagery. Contemporary imagery research has now broadly extended its scope to include every experience that resembles the experience of perceiving from any sensorial modality. The underlying neurocognitive mechanisms involved in mental imagery, however, and the subsequent physical performance, are still far from being fully understood. Understanding the processes behind the human ability to create mental images of events and experiences, remains a critical issue. Recent research, both in experimental as well as practical contexts, suggests that imagined and executed movement planning relies on internal models for action (Hesslow, 2012). These representations are frequently associated with the notion of internal (forward) models and are hypothesized to be an integral part of action planning (Wolpert, 1997; Skoura, Vinter, & Papaxanthis, 2009). Furthermore, Steenbergen, van Nimwegen, and Crajé (2007) suggest that motor imagery may be a necessary prerequisite for motor planning. Jeannerod (2001) studied the role of motor imagery in action planning and proposed the so-called equivalence hypothesis, suggesting that motor simulation and motor control processes are functionally equivalent (Munzert, Lorey, & Zentgraf, 2009; Ramsey, Cumming, Eastough, & Edwards, 2010). Advances in information and communication technologies have made new tools available to scientists interested in artificial cognitive systems and in designing robotic platforms equipped with sophisticated motors and sensors in order to replicate animal or human sensorimotor input/output streams, e.g. the iCub humanoid robot (Metta, Natale, Nori, Sandini, Vernon, Fadiga, et al., 2010). These platforms, despite the tremendous potential applications, still face several challenges in developing complex behaviors (Asada et al., 2009). To this end, increased research efforts are needed to understand the role of mental imagery and its mechanisms in human cognition and how it can be used to enhance motor control in autonomous robots. From a technological point of view, the impact in the field of robotics could be significant. It could lead to the derivation of engineering principles for the development of autonomous systems that are capable of

Short communication: Psychology with soft computing: An integrated approach and its applications

Soft computing techniques proved to be successful in many application areas. In this paper we investigate the application in psychopathological field of two well known soft computing techniques, fuzzy logic and genetic algorithms (GAs). The investigation started from a practical need: the creation of a tool for a quick and correct classification of mental retardation level, which is needed to choose the right treatment for rehabilitation and to assure a quality of life that is suitable for the specific patient condition. In order to meet this need we researched an adaptive data mining technique that allows us to build interpretable models for automatic and reliable diagnosis. Our work concerned a genetic fuzzy system (GFS), which integrates a classical GA and the fuzzy C-means (FCM) algorithm. This GFS, called genetic fuzzy C-means (GFCM), is able to select the best subset of features to generate an efficient classifier for diagnostic purposes from a database of examples. Additionally, thanks to an extension of the FCM algorithm, the proposed technique could also handle databases with missing values. The results obtained in a practical application on a real database of patients and comparisons with established techniques showed the efficiency of the integrated algorithm, both in data mining and completion.