90 research outputs found
Overcoming device unreliability with continuous learning in a population coding based computing system
The brain, which uses redundancy and continuous learning to overcome the
unreliability of its components, provides a promising path to building
computing systems that are robust to the unreliability of their constituent
nanodevices. In this work, we illustrate this path by a computing system based
on population coding with magnetic tunnel junctions that implement both neurons
and synaptic weights. We show that equipping such a system with continuous
learning enables it to recover from the loss of neurons and makes it possible
to use unreliable synaptic weights (i.e. low energy barrier magnetic memories).
There is a tradeoff between power consumption and precision because low energy
barrier memories consume less energy than high barrier ones. For a given
precision, there is an optimal number of neurons and an optimal energy barrier
for the weights that leads to minimum power consumption
OvA-INN: Continual Learning with Invertible Neural Networks
In the field of Continual Learning, the objective is to learn several tasks
one after the other without access to the data from previous tasks. Several
solutions have been proposed to tackle this problem but they usually assume
that the user knows which of the tasks to perform at test time on a particular
sample, or rely on small samples from previous data and most of them suffer of
a substantial drop in accuracy when updated with batches of only one class at a
time. In this article, we propose a new method, OvA-INN, which is able to learn
one class at a time and without storing any of the previous data. To achieve
this, for each class, we train a specific Invertible Neural Network to extract
the relevant features to compute the likelihood on this class. At test time, we
can predict the class of a sample by identifying the network which predicted
the highest likelihood. With this method, we show that we can take advantage of
pretrained models by stacking an Invertible Network on top of a feature
extractor. This way, we are able to outperform state-of-the-art approaches that
rely on features learning for the Continual Learning of MNIST and CIFAR-100
datasets. In our experiments, we reach 72% accuracy on CIFAR-100 after training
our model one class at a time.Comment: to be published in IJCNN 202
Microwave neural processing and broadcasting with spintronic nano-oscillators
Can we build small neuromorphic chips capable of training deep networks with
billions of parameters? This challenge requires hardware neurons and synapses
with nanometric dimensions, which can be individually tuned, and densely
connected. While nanosynaptic devices have been pursued actively in recent
years, much less has been done on nanoscale artificial neurons. In this paper,
we show that spintronic nano-oscillators are promising to implement analog
hardware neurons that can be densely interconnected through electromagnetic
signals. We show how spintronic oscillators maps the requirements of artificial
neurons. We then show experimentally how an ensemble of four coupled
oscillators can learn to classify all twelve American vowels, realizing the
most complicated tasks performed by nanoscale neurons
Tunable Superconducting Properties of a-NbSi Thin Films and Application to Detection in Astrophysics
We report on the superconducting properties of amorphous NbxSi1-x thin films.
The normal-state resistance and critical temperatures can be separately
adjusted to suit the desired application. Notably, the relatively low
electron-phonon coupling of these films makes them good candidates for an "all
electron bolometer" for Cosmological Microwave Background radiation detection.
Moreover, this device can be made to suit both high and low impedance readouts
Role of non-linear data processing on speech recognition task in the framework of reservoir computing
The reservoir computing neural network architecture is widely used to test
hardware systems for neuromorphic computing. One of the preferred tasks for
bench-marking such devices is automatic speech recognition. However, this task
requires acoustic transformations from sound waveforms with varying amplitudes
to frequency domain maps that can be seen as feature extraction techniques.
Depending on the conversion method, these may obscure the contribution of the
neuromorphic hardware to the overall speech recognition performance. Here, we
quantify and separate the contributions of the acoustic transformations and the
neuromorphic hardware to the speech recognition success rate. We show that the
non-linearity in the acoustic transformation plays a critical role in feature
extraction. We compute the gain in word success rate provided by a reservoir
computing device compared to the acoustic transformation only, and show that it
is an appropriate benchmark for comparing different hardware. Finally, we
experimentally and numerically quantify the impact of the different acoustic
transformations for neuromorphic hardware based on magnetic nano-oscillators.Comment: 13 pages, 5 figure
Spin Channels in Functionalized Graphene Nanoribbons
We characterize the transport properties of functionalized graphene
nanoribbons using extensive first-principles calculations based on density
functional theory (DFT) that encompass both monovalent and divalent ligands,
hydrogenated defects and vacancies. We find that the edge metallic states are
preserved under a variety of chemical environments, while bulk conducting
channels can be easily destroyed by either hydrogenation or ion or electron
beams, resulting in devices that can exhibit spin conductance polarization
close to unity.Comment: 14 pages, 5 figure
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