148 research outputs found
Stochastic IMT (insulator-metal-transition) neurons: An interplay of thermal and threshold noise at bifurcation
Artificial neural networks can harness stochasticity in multiple ways to
enable a vast class of computationally powerful models. Electronic
implementation of such stochastic networks is currently limited to addition of
algorithmic noise to digital machines which is inherently inefficient; albeit
recent efforts to harness physical noise in devices for stochasticity have
shown promise. To succeed in fabricating electronic neuromorphic networks we
need experimental evidence of devices with measurable and controllable
stochasticity which is complemented with the development of reliable
statistical models of such observed stochasticity. Current research literature
has sparse evidence of the former and a complete lack of the latter. This
motivates the current article where we demonstrate a stochastic neuron using an
insulator-metal-transition (IMT) device, based on electrically induced
phase-transition, in series with a tunable resistance. We show that an IMT
neuron has dynamics similar to a piecewise linear FitzHugh-Nagumo (FHN) neuron
and incorporates all characteristics of a spiking neuron in the device
phenomena. We experimentally demonstrate spontaneous stochastic spiking along
with electrically controllable firing probabilities using Vanadium Dioxide
(VO) based IMT neurons which show a sigmoid-like transfer function. The
stochastic spiking is explained by two noise sources - thermal noise and
threshold fluctuations, which act as precursors of bifurcation. As such, the
IMT neuron is modeled as an Ornstein-Uhlenbeck (OU) process with a fluctuating
boundary resulting in transfer curves that closely match experiments. As one of
the first comprehensive studies of a stochastic neuron hardware and its
statistical properties, this article would enable efficient implementation of a
large class of neuro-mimetic networks and algorithms.Comment: Added sectioning, Figure 6, Table 1, and Section II.E Updated
abstract, discussion and corrected typo
Direct Feedback Alignment with Sparse Connections for Local Learning
Recent advances in deep neural networks (DNNs) owe their success to training
algorithms that use backpropagation and gradient-descent. Backpropagation,
while highly effective on von Neumann architectures, becomes inefficient when
scaling to large networks. Commonly referred to as the weight transport
problem, each neuron's dependence on the weights and errors located deeper in
the network require exhaustive data movement which presents a key problem in
enhancing the performance and energy-efficiency of machine-learning hardware.
In this work, we propose a bio-plausible alternative to backpropagation drawing
from advances in feedback alignment algorithms in which the error computation
at a single synapse reduces to the product of three scalar values. Using a
sparse feedback matrix, we show that a neuron needs only a fraction of the
information previously used by the feedback alignment algorithms. Consequently,
memory and compute can be partitioned and distributed whichever way produces
the most efficient forward pass so long as a single error can be delivered to
each neuron. Our results show orders of magnitude improvement in data movement
and improvement in multiply-and-accumulate operations over
backpropagation. Like previous work, we observe that any variant of feedback
alignment suffers significant losses in classification accuracy on deep
convolutional neural networks. By transferring trained convolutional layers and
training the fully connected layers using direct feedback alignment, we
demonstrate that direct feedback alignment can obtain results competitive with
backpropagation. Furthermore, we observe that using an extremely sparse
feedback matrix, rather than a dense one, results in a small accuracy drop
while yielding hardware advantages. All the code and results are available
under https://github.com/bcrafton/ssdfa.Comment: 15 pages, 8 figure
Bipolar effects in unipolar junctionless transistors
In this work, we analyze hysteresis and bipolar effects in unipolar junctionless transistors. A change in subthreshold drain current by 5 orders of magnitude is demonstrated at a drain voltage of 2.25 V in silicon junctionless transistor. Contrary to the conventional theory, increasing gate oxide thickness results in (i) a reduction of subthreshold slope (S-slope) and (ii) an increase in drain current, due to bipolar effects. The high sensitivity to film thickness in junctionless devices will be most crucial factor in achieving steep transition from ON to OFF state. (C) 2012 American Institute of Physics. (http://dx.doi.org/10.1063/1.4748909
Direct Feedback Alignment With Sparse Connections for Local Learning
Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on von Neumann architectures, becomes inefficient when scaling to large networks. Commonly referred to as the weight transport problem, each neuron's dependence on the weights and errors located deeper in the network require exhaustive data movement which presents a key problem in enhancing the performance and energy-efficiency of machine-learning hardware. In this work, we propose a bio-plausible alternative to backpropagation drawing from advances in feedback alignment algorithms in which the error computation at a single synapse reduces to the product of three scalar values. Using a sparse feedback matrix, we show that a neuron needs only a fraction of the information previously used by the feedback alignment algorithms. Consequently, memory and compute can be partitioned and distributed whichever way produces the most efficient forward pass so long as a single error can be delivered to each neuron. We evaluate our algorithm using standard datasets, including ImageNet, to address the concern of scaling to challenging problems. Our results show orders of magnitude improvement in data movement and 2× improvement in multiply-and-accumulate operations over backpropagation. Like previous work, we observe that any variant of feedback alignment suffers significant losses in classification accuracy on deep convolutional neural networks. By transferring trained convolutional layers and training the fully connected layers using direct feedback alignment, we demonstrate that direct feedback alignment can obtain results competitive with backpropagation. Furthermore, we observe that using an extremely sparse feedback matrix, rather than a dense one, results in a small accuracy drop while yielding hardware advantages. All the code and results are available under https://github.com/bcrafton/ssdfa
Utilizing switched linear dynamics of interconnected state transition devices for approximating certain global functions
The objective of the proposed research is to create alternative computing models and architectures, unlike (discrete) sequential Turing machine/Von Neumann style models, which utilize the network dynamics of interconnected IMT (insulator-metal transition) devices. This work focusses on circuits (mainly coupled oscillators) and the resulting switched linear dynamical systems that arise in networks of IMT devices. Electrical characteristics of the devices and their stochasticity are modeled mathematically and used to explain experimentally observed behavior. For certain kinds of connectivity patterns, the steady state limit cycles of these systems encode approximate solutions to global functions like dominant eigenvector of the connectivity matrix and graph coloring of the connectivity graph.Ph.D
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