A network processor for a learning based routing protocol

Recently, Cognitive Packet Networks (CPN) is proposed as an alternative to the IP based network architectures and shows similarity with the discrete active networks. In CPN, there is no routing table, instead reinforcement learning (Random Neural Networks) is used to route packets. CPN routes packets based on QoS, using measurements that are constantly collected by packets and deposited in mailboxes at routers. The applicability of the CPN concept has been demonstrated through several software implementations. However, higher data traffic and increasing packet processing demands require the implementation of this new network architecture in hardware. In this paper, we present a network processor architecture which supports this learning based protocol. ©2004 IEEE

A road towards the photonic hardware implementation of artificial cognitive circuits

Many technologies we use are inspired by nature. This happens in different domains, ranging from mechanics to optics to computer sciences. Nature has incredible potentialities that man still does not know or that he striving to learn through experience. These potentialities concern the ability to solve complex problems through approaches of various types of distributed intelligence. In fact, there are forms of intelligence in nature that differ from that of man, but are nevertheless exceedingly efficient. Man has often used as a model those forms of distributed intelligence that allow colonies of animals to develop places of housing or collective behaviors of extreme complexity. Recently, M. Alonzo et alii (Sci.Rep. 8, 5716 (2018)) published a hardware implementation to solve complex routing problems in modern information networks by exploiting the immense possibilities offered by light. This article presents an addressable photonic circuit based on the decision-making processes of ant colonies looking for food. When ants search for food, they modify their surroundings by leaving traces of pheromone, which may be reinforced and function as a type of path marker for when food has been found. This process is based on stigmergy, or the modification of the environment to implement distributed decision-making processes. The photonic hardware implementation that this work proposes is a photonic X-junction that simulates this stigmergic procedure. The experimental implementation is based on the use of non-linear substrates, i.e. materials that can be modified by light, simulating the modification induced by the ants on the surrounding environment when they leave the pheromone traces. Here, two laser beams generate two crossing channels in which the index of refraction is increased with respect to the whole substrate. These channels act as integrated waveguides (almost self-written optical fibers) within which optical information can be propagated (as happens for the ants that follow traces of pheromone already “written”). The proposed device is a X-junction with two crossing waveguides, whose refractive index contrast is defined by the intensities of the writing light beams. The higher the writing intensity, the greater the induced index variation, as if it were an increasingly intense pheromone trace. The information will follow the most contrasted harm of the junction, which is driven and eventually switched by the writing light intensity. Any optical information that will be sent to the device will follow the most intense trace, i.e. the most contrasted waveguide. The paper demonstrates a device that can be wholly operated using the light and that can be the basis of complex hardware configurations that might reproduce the stigmergic distributed intelligence. This is a highly significant innovation in the field of electronic and photonic technologies, within which artificial cognition and decision processes are implemented into a hardware circuit and not in a software code