FRANCESCHI M.

The development of embedded and real-time systems for Machine Learning data processing is challenging (e.g. IoT). Low latency, low power consumption and reduced hardware complexity should be the characteristics of such systems. Considering prosthetic applications, which are error-tolerant, a technique that tunes the precision of operands and...

Approximate computing has emerged as a promising approach to energy-efficient design of digital systems in many domains such as digital signal processing, robotics, and machine learning. Numerous studies report that employing different data formats in Deep Neural Networks (DNNs), the dominant Machine Learning approach, could allow substantial...

In most robotic and biomedical applications, the interest for real-time embedded systems with tactile ability has been growing. For example in prosthetics, a dedicated portable system is needed for developing wearable devices. The main challenges for such systems are low latency, low power consumption and reduced hardware complexity. In order...

Modern prostheses aim at restoring the functional and aesthetic characteristics of the lost limb. To foster prosthesis embodiment and functionality, it is necessary to re-establish both volitional control and sensory feedback. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two...

Closing the prosthesis control loop by providing tactile sensory feedback to the user is a key point in research on active prosthetics as well as an often cited requirement of the prosthesis users. © 2018 IEEE.