107 research outputs found
Integer Echo State Networks: Hyperdimensional Reservoir Computing
We propose an approximation of Echo State Networks (ESN) that can be
efficiently implemented on digital hardware based on the mathematics of
hyperdimensional computing. The reservoir of the proposed Integer Echo State
Network (intESN) is a vector containing only n-bits integers (where n<8 is
normally sufficient for a satisfactory performance). The recurrent matrix
multiplication is replaced with an efficient cyclic shift operation. The intESN
architecture is verified with typical tasks in reservoir computing: memorizing
of a sequence of inputs; classifying time-series; learning dynamic processes.
Such an architecture results in dramatic improvements in memory footprint and
computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl
An Approach for Self-Adaptive Path Loss Modelling for Positioning in Underground Environments
This paper proposes a real-time self-adaptive approach for accurate path loss estimation in underground mines or tunnels based on signal strength measurements from heterogeneous radio communication technologies. The proposed model features simplicity of implementation. The methodology is validated in simulations and verified by measurements taken in real environments. The proposed method leverages accuracy of positioning matching the existing approaches while requiring smaller engineering efforts
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks
A change of the prevalent supervised learning techniques is foreseeable in
the near future: from the complex, computational expensive algorithms to more
flexible and elementary training ones. The strong revitalization of randomized
algorithms can be framed in this prospect steering. We recently proposed a
model for distributed classification based on randomized neural networks and
hyperdimensional computing, which takes into account cost of information
exchange between agents using compression. The use of compression is important
as it addresses the issues related to the communication bottleneck, however,
the original approach is rigid in the way the compression is used. Therefore,
in this work, we propose a more flexible approach to compression and compare it
to conventional compression algorithms, dimensionality reduction, and
quantization techniques.Comment: 12 pages, 3 figure
Heteroclinic cycles and chaos in a system of four identical phase oscillators with global biharmonic coupling
We study a system of four identical globally coupled phase oscillators with
biharmonic coupling function. Its dimension and the type of coupling make it
the minimal system of Kuramoto-type (both in the sense of the phase space's
dimension and the number of harmonics) that supports chaotic dynamics. However,
to the best of our knowledge, there is still no numerical evidence for the
existence of chaos in this system. The dynamics of such systems is tightly
connected with the action of the symmetry group on its phase space. The
presence of symmetries might lead to an emergence of chaos due to scenarios
involving specific heteroclinic cycles. We suggest an approach for searching
such heteroclinic cycles and showcase first examples of chaos in this system
found by using this approach.Comment: 18 pages, 8 figure
Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
The deployment of machine learning algorithms on resource-constrained edge
devices is an important challenge from both theoretical and applied points of
view. In this article, we focus on resource-efficient randomly connected neural
networks known as Random Vector Functional Link (RVFL) networks since their
simple design and extremely fast training time make them very attractive for
solving many applied classification tasks. We propose to represent input
features via the density-based encoding known in the area of stochastic
computing and use the operations of binding and bundling from the area of
hyperdimensional computing for obtaining the activations of the hidden neurons.
Using a collection of 121 real-world datasets from the UCI Machine Learning
Repository, we empirically show that the proposed approach demonstrates higher
average accuracy than the conventional RVFL. We also demonstrate that it is
possible to represent the readout matrix using only integers in a limited range
with minimal loss in the accuracy. In this case, the proposed approach operates
only on small n-bits integers, which results in a computationally efficient
architecture. Finally, through hardware FPGA implementations, we show that such
an approach consumes approximately eleven times less energy than that of the
conventional RVFL.Comment: 7 pages, 7 figure
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