5,457 research outputs found
Using Collective Intelligence to Route Internet Traffic
A COllective INtelligence (COIN) is a set of interacting reinforcement
learning (RL) algorithms designed in an automated fashion so that their
collective behavior optimizes a global utility function. We summarize the
theory of COINs, then present experiments using that theory to design COINs to
control internet traffic routing. These experiments indicate that COINs
outperform all previously investigated RL-based, shortest path routing
algorithms.Comment: 7 page
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Neuromorphic computing systems comprise networks of neurons that use
asynchronous events for both computation and communication. This type of
representation offers several advantages in terms of bandwidth and power
consumption in neuromorphic electronic systems. However, managing the traffic
of asynchronous events in large scale systems is a daunting task, both in terms
of circuit complexity and memory requirements. Here we present a novel routing
methodology that employs both hierarchical and mesh routing strategies and
combines heterogeneous memory structures for minimizing both memory
requirements and latency, while maximizing programming flexibility to support a
wide range of event-based neural network architectures, through parameter
configuration. We validated the proposed scheme in a prototype multi-core
neuromorphic processor chip that employs hybrid analog/digital circuits for
emulating synapse and neuron dynamics together with asynchronous digital
circuits for managing the address-event traffic. We present a theoretical
analysis of the proposed connectivity scheme, describe the methods and circuits
used to implement such scheme, and characterize the prototype chip. Finally, we
demonstrate the use of the neuromorphic processor with a convolutional neural
network for the real-time classification of visual symbols being flashed to a
dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure
Managing network congestion with a Kohonen-based RED queue
The behaviour of the TCP AIMD algorithm is known to cause queue length
oscillations when congestion occurs at a router output link. Indeed, due to
these queueing variations, end-to-end applications experience large delay
jitter. Many studies have proposed efficient Active Queue Management (AQM)
mechanisms in order to reduce queue oscillations and stabilize the queue
length. These AQM are mostly improvements of the Random Early Detection (RED)
model. Unfortunately, these enhancements do not react in a similar manner for
various network conditions and are strongly sensitive to their initial setting
parameters. Although this paper proposes a solution to overcome the
difficulties of setting these parameters by using a Kohonen neural network
model, another goal of this study is to investigate whether cognitive
intelligence could be placed in the core network to solve such stability
problem. In our context, we use results from the neural network area to
demonstrate that our proposal, named Kohonen-RED (KRED), enables a stable queue
length without complex parameters setting and passive measurements.Comment: 8 pages, 9 figure
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