Spanish National Research Council · University of Seville
 HOME
INTRANET
sp    en

Funding for the research activities carried out at IMSE-CNM comes primarily from the participation in competitive tender processes. The research is then conducted out via agreements, projects and contracts with national and international public organizations and private companies and organizations.



MEM-SCALES
Memory technologies with multi-scale time constants for neuromorphic architectures
PI: Bernabé Linares Barranco
[+]
Type: Research project
Reference: H2020 ICT-06-2019, Ref: 871371
Funding Body: European Union
Start date: 01/01/2020
End date: 31/12/2022
Funding: 569.926,00 €
Abstract: The project MeM-Scales aims at lifting neuromorphic computing in analog spiking microprocessors to an entirely new level of performance. Work in this project is based on a dedicated commitment that novel hardware and novel computational concepts must be co-evolved in a close interaction between nano-electronic device engineering, circuit and microprocessor design, fabrication technology and computing science (machine learning and nonlinear modeling). A key to reflecting ‘hardware physics ’ in ‘computational function ’ and vice versa is the fundamental role played by multiple timescales. Here MeM-Scales introduces a number of innovations. On the side of physical substrates, novel memory and device technologies, supporting on-chip learning over multiple timescales for both synapses and neurons, will be fabricated. To enable timescales spanning up to 9 (!) orders of magnitude both volatile memory and non-volatile memory as well as Thin Film Transistor technology will be exploited. On the side of computational theory, autonomous learning algorithms and architectures supporting computation over these wide range of timescales will be developed. These computational methods are specifically tailored to cope with the low numerical precision, parameter drift, stochasticity, and device mismatch which are inherent in analog nano-scale devices. These cross-disciplinary efforts will lead to the fabrication of an innovative hardware/ software platform as a basis for future products which combine extreme power efficiency with robust cognitive computing capabilities. This new kind of computing technology will open new perspectives, for instance, for high-dimensional distributed environmental monitoring, implantable medical diagnostic microchips, wearable electronics or human-computer interfacing.

NEURONN
Two-Dimensional Oscillatory Neural Networks for Energy Efficent Neuromorphic computing
PI: Bernabé Linares Barranco Press Release
[+]
Type: Research project
Reference: H2020 ICT-06-2019, Ref: 871501
Funding Body: European Union
Start date: 01/01/2020
End date: 31/12/2022
Funding: 589.440,00 €
Abstract: Neuro-inspired computing architectures are one of the leading candidates to solve complex and large-scale associative learning problems for AI applications. The two key building blocks for neuromorphic computing are the neuron and the synapse, which form the distributed computing and memory units. In the NeurONN project, we are proposing a novel neuroinspired computing architecture where information is encoded in the ‘phase’ of coupled oscillating neurons or oscillatory neural networks (ONN). Specifically, VO2 metal-insulator transition (MIT) devices and 2D memristors will be developed as neurons and synapses for hardware implementations. We predict VO2 MIT devices are up to 250X more energy efficient than state of the art digital CMOS based oscillators, where 2D memristors are up to 330X more energy efficient than state of the art TiO2 memristors. Moreover, the predicted energy efficiency gain of ONN architecture vs state of the art spiking neural network (SNN) architecture is up to 40X. Thus, NeurONN will showcase a novel and alternative energy efficient neuromorphic computing paradigm based on energy efficient devices and architectures. Such ONN will demonstrate synchronization and coupling dynamics for establishing collective learning behavior, in addition to desirable characteristics such as scaling, ultra-low power computation, and high computing performance. NeurONN aims to develop the first-ever ONN hardware platform (targeting two demonstrators) and complete with an ONN design methodology toolbox covering aspects from ONN architecture design to algorithms in order to facilitate adoption, testing and experimentation of ONN demonstrator chips by all potential users to unleash the potential of ONN technology.

ECOMODE
Event-driven compressive vision for multimodal interaction with mobile devices
PI: Bernabé Linares Barranco web
[+]
Type: Research project
Reference: 644096
Funding Body: European Union
Start date: 01/01/2015
End date: 31/12/2018
Funding: 556.278,75 €
Abstract: The visually impaired and the elderly, often suffering from mild speech and/or motor disabilities, are experiencing a significant and increasing barrier in accessing ICT technology and services. Yet, in order to be able to participate in a modern, interconnected society that relies on ICT technologies for handling everyday issues, there is clear need also for these user groups to have access to ICT, in particular to mobile platforms such as tablet computers or smart-phones. The proposed project aims at developing and exploiting the recently matured and quickly advancing biologically-inspired technology of event-driven, compressive sensing (EDC) of audio-visual information, to realize a new generation of low-power multi-modal human-computer interface for mobile devices.
The project is based on two main technology pillars: (A) an air gesture control set, and (B) a vision-assisted speech recognition set. (A) exploits EDC vision for low and high level hand and finger gesture recognition and subsequent command execution; (B) combines temporal dynamics from lip and chin motion acquired using EDC vision sensors with the auditory sensor input to gain robustness and background noise immunity of spoken command recognition and speech-to-text input. In contrast to state-of-the-art technologies, both proposed human-computer communication channels will be designed to work reliably under uncontrolled conditions. Particularly, mobile devices equipped with the proposed interface technology will facilitate unrestricted outdoor use under uncontrolled lighting and background noise conditions. Furthermore, due to the sparse nature of information encoding, EDC excels conventional approaches in energy efficiency, yielding an ideal solution for mobile, battery-powered devices.
ECOMODE is committed to pave the way for industrialization of commercial products by demonstrating the availability of the required hardware and software components and their integrability into a mobile platform.