Section: New Results

Embodied and embedded systems

Participants : Yann Boniface, Hervé Frezza-Buet, Bernard Girau, Mathieu Lefort, Dominique Martinez, Jean-Charles Quinton, Nicolas Rougier.

InterCell

Our research in the field of dedicated architectures and connectionist parallelism mostly focuses on embedded systems (cf.  § 3.5 ). Nevertheless we are also involved in a new project that considers coarse-grain parallel machines as implementation devices. The core idea of this InterCell project (part of the MIS axis of the CPER (cf.  § 8.1 ); cf. also http://intercell.metz.supelec.fr ) is to map fine grain computation (cells) to the actual structure of PC clusters. The latter rather fit coarse grain processing, using relatively few packed communication, which a priori contradicts neural computing. Another fundamental feature of the InterCell project is to promote interaction between the parallel process and the external world. Both features, cellular computing and interaction, allow to consider the use of neural architectures on the cluster on-line, for the control of situated systems, as robots.

This year, the whole setting up of interactive cellular computation has been realized. It consists of the booz library, released by Hervé Frezza-Buet, that allows the design of cellular computation, providing tools for vizualization, savings, step-by-step execution, on-line communication with the external world. From this core library, the implementation of the escabooz suite has been achieved, allowing to solve PDEs by cellular computation. The Intercell cluster is thus available for such a purpose, rather oriented toward physicists. The implementation of cortically inspired neural networks (the bijama model) is at work, as well as interfaces for integrating on the cluster visual units coupled with a video device, for situated robotic experiments mainly.

Embodied/embedded olfactory systems

In the framework of the associate team BioSens, we constructed a micro-electronic nose model using a semiconductor gas sensor array which incorporates spiking neurons encoding sensory information as suggested by the time-to-first-spike paradigm. This study pioneers the translation of neurophysiological findings into hardware for the processing of electronic noses. Another example of bio-inspired processing is our autonomous olfactory robot, for which we have implemented the novel probabilistic technique Infotaxis of Vergassola et al.: we have shown that, although animal behavioral patterns are not pre-programmed or imposed through explicit rules of movement, these behaviors do actually emerge naturally from the underlying model. New improvements of these systems are currently studied.

Specific hardware implementations

In the field of dedicated embeddable neural implementations, we use our expertise in both neural networks and FPGAs so as to propose efficient implementations of applied neural networks on FPGAs.

Recent works in this axis have mainly focused on implementations of spiking neural models with on-chip learning. This work takes advantage of a highly modular and flexible architecture that is able to fit various hardware constraints and parallelism levels. It mainly consists of a population hardware coding module based on bio-inspired gaussian receptive fields [31] , and a spiking neural computation module with or without on-chip learning (using the SpikeProp algorithm) [32] . This work has been carried out within the activities of the CorTexMex associate team (cf.  § 8.4 ).

Brain-inspired hardware

Our activities on dedicated architectures have strongly evolved in the last years. We now focus on the definition of brain-inspired hardware-adapted frameworks of neural computation. The long-term goal is to define and implement modular and extensive resources that are capable of self-organization and self-recruitment through learning when they are assembled within a perception-action loop. This goal gathers our expertise in neural hardware implementations and behavioral models for sensori-motor tasks.

This year, we have mostly carried out upstream studies that still need a hardware development:

• Our works are based here on dynamic neural fields. In order to cope with hardware connectivity requirements, we have defined a model of dynamic spiking neural fields (in the context of visual attention) that only handles local lateral connections within bio-inspired maps of spiking neurons (see § 6.2 ).

• We also address the problem of the costful local storage of lateral kernels by defining hardware-friendly lateral kernels based on non-euclidean norms or random generation of local influences.

• We have carried out upstream studies to define hardware-compatible protocols to assemble various perception-action modalities that are implemented and associated by different bio-inspired neural maps. The hardware plausibility of this model requires simplified local interconnections. We have introduced a new perceptive level that only needs a local feedback interaction from the competitive layer (see the paragraph on “Multimodal learning” in § 6.2 ). We intend to mix this approach with our definition of spiking neural fields (at the competitive level), to be able to satisfy the hardware constraints of the assembling of neural maps.