293 research outputs found
Self-Organizing Machine Architecture
International audienceSOMA is a France-Switzerland collaborative project which aims to develop a computing machine with self-organizing properties inspired by the functioning of the brain. The SOMA project addresses this challenge by lying at the intersection of four main research fields, namely adaptive reconfigurable computing, cellular computing, computational neuroscience, and neuromorphic engineering. In the framework of SOMA, we designed the SCALP platform, a 3D array of FPGAs and processors permitting to prototype and evaluate self-organization mechanisms on physical cellular machines
Neuromorphic hardware as a self-organizing computing system
International audienceThis paper presents the self-organized neuromorphic architecture named SOMA. The objective is to study neural-based self-organization in computing systems and to prove the feasibility of a self-organizing hardware structure. Considering that these properties emerge from large scale and fully connected neural maps, we will focus on the definition of a self-organizing hardware architecture based on digital spiking neurons that offer hardware efficiency. From a biological point of view, this corresponds to a combination of the so-called synaptic and structural plasticities. We intend to define computational models able to simultaneously self-organize at both computation and communication levels, and we want these models to be hardware-compliant, fault tolerant and scalable by means of a neuro-cellular structure
The importance of space and time in neuromorphic cognitive agents
Artificial neural networks and computational neuroscience models have made
tremendous progress, allowing computers to achieve impressive results in
artificial intelligence (AI) applications, such as image recognition, natural
language processing, or autonomous driving. Despite this remarkable progress,
biological neural systems consume orders of magnitude less energy than today's
artificial neural networks and are much more agile and adaptive. This
efficiency and adaptivity gap is partially explained by the computing substrate
of biological neural processing systems that is fundamentally different from
the way today's computers are built. Biological systems use in-memory computing
elements operating in a massively parallel way rather than time-multiplexed
computing units that are reused in a sequential fashion. Moreover, activity of
biological neurons follows continuous-time dynamics in real, physical time,
instead of operating on discrete temporal cycles abstracted away from
real-time. Here, we present neuromorphic processing devices that emulate the
biological style of processing by using parallel instances of mixed-signal
analog/digital circuits that operate in real time. We argue that this approach
brings significant advantages in efficiency of computation. We show examples of
embodied neuromorphic agents that use such devices to interact with the
environment and exhibit autonomous learning
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