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Implementing size-optimal discrete neural networks require analog circuitry
This paper starts by overviewing results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov`s superpositions the authors show that implementing Boolean functions can be done using neurons having an identity transfer function. Because in this case the size of the network is minimized, it follows that size-optimal solutions for implementing Boolean functions can be obtained using analog circuitry. Conclusions and several comments on the required precision are ending the paper
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
On metrics of density and power efficiency, neuromorphic technologies have
the potential to surpass mainstream computing technologies in tasks where
real-time functionality, adaptability, and autonomy are essential. While
algorithmic advances in neuromorphic computing are proceeding successfully, the
potential of memristors to improve neuromorphic computing have not yet born
fruit, primarily because they are often used as a drop-in replacement to
conventional memory. However, interdisciplinary approaches anchored in machine
learning theory suggest that multifactor plasticity rules matching neural and
synaptic dynamics to the device capabilities can take better advantage of
memristor dynamics and its stochasticity. Furthermore, such plasticity rules
generally show much higher performance than that of classical Spike Time
Dependent Plasticity (STDP) rules. This chapter reviews the recent development
in learning with spiking neural network models and their possible
implementation with memristor-based hardware
Spatio-temporal Learning with Arrays of Analog Nanosynapses
Emerging nanodevices such as resistive memories are being considered for
hardware realizations of a variety of artificial neural networks (ANNs),
including highly promising online variants of the learning approaches known as
reservoir computing (RC) and the extreme learning machine (ELM). We propose an
RC/ELM inspired learning system built with nanosynapses that performs both
on-chip projection and regression operations. To address time-dynamic tasks,
the hidden neurons of our system perform spatio-temporal integration and can be
further enhanced with variable sampling or multiple activation windows. We
detail the system and show its use in conjunction with a highly analog
nanosynapse device on a standard task with intrinsic timing dynamics- the TI-46
battery of spoken digits. The system achieves nearly perfect (99%) accuracy at
sufficient hidden layer size, which compares favorably with software results.
In addition, the model is extended to a larger dataset, the MNIST database of
handwritten digits. By translating the database into the time domain and using
variable integration windows, up to 95% classification accuracy is achieved. In
addition to an intrinsically low-power programming style, the proposed
architecture learns very quickly and can easily be converted into a spiking
system with negligible loss in performance- all features that confer
significant energy efficiency.Comment: 6 pages, 3 figures. Presented at 2017 IEEE/ACM Symposium on Nanoscale
architectures (NANOARCH
Photonic Neural Networks and Optics-informed Deep Learning Fundamentals
The recent explosive compute growth, mainly fueled by the boost of AI and
DNNs, is currently instigating the demand for a novel computing paradigm that
can overcome the insurmountable barriers imposed by conventional electronic
computing architectures. PNNs implemented on silicon integration platforms
stand out as a promising candidate to endow NN hardware, offering the potential
for energy efficient and ultra-fast computations through the utilization of the
unique primitives of photonics i.e. energy efficiency, THz bandwidth and
low-latency. Thus far, several demonstrations have revealed the huge potential
of PNNs in performing both linear and non-linear NN operations at unparalleled
speed and energy consumption metrics. Transforming this potential into a
tangible reality for DL applications requires, however, a deep understanding of
the basic PNN principles, requirements and challenges across all constituent
architectural, technological and training aspects. In this tutorial, we,
initially, review the principles of DNNs along with their fundamental building
blocks, analyzing also the key mathematical operations needed for their
computation in a photonic hardware. Then, we investigate, through an intuitive
mathematical analysis, the interdependence of bit precision and energy
efficiency in analog photonic circuitry, discussing the opportunities and
challenges of PNNs. Followingly, a performance overview of PNN architectures,
weight technologies and activation functions is presented, summarizing their
impact in speed, scalability and power consumption. Finally, we provide an
holistic overview of the optics-informed NN training framework that
incorporates the physical properties of photonic building blocks into the
training process in order to improve the NN classification accuracy and
effectively elevate neuromorphic photonic hardware into high-performance DL
computational settings
Real-time support for high performance aircraft operation
The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated. Research results on ANN structures for real-time applications are given. Research results on ANN algorithms for real-time control are also shown
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