7,496 research outputs found
Convolutional Radio Modulation Recognition Networks
We study the adaptation of convolutional neural networks to the complex
temporal radio signal domain. We compare the efficacy of radio modulation
classification using naively learned features against using expert features
which are widely used in the field today and we show significant performance
improvements. We show that blind temporal learning on large and densely encoded
time series using deep convolutional neural networks is viable and a strong
candidate approach for this task especially at low signal to noise ratio
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
Wave-based extreme deep learning based on non-linear time-Floquet entanglement
Wave-based analog signal processing holds the promise of extremely fast,
on-the-fly, power-efficient data processing, occurring as a wave propagates
through an artificially engineered medium. Yet, due to the fundamentally weak
non-linearities of traditional wave materials, such analog processors have been
so far largely confined to simple linear projections such as image edge
detection or matrix multiplications. Complex neuromorphic computing tasks,
which inherently require strong non-linearities, have so far remained
out-of-reach of wave-based solutions, with a few attempts that implemented
non-linearities on the digital front, or used weak and inflexible non-linear
sensors, restraining the learning performance. Here, we tackle this issue by
demonstrating the relevance of Time-Floquet physics to induce a strong
non-linear entanglement between signal inputs at different frequencies,
enabling a power-efficient and versatile wave platform for analog extreme deep
learning involving a single, uniformly modulated dielectric layer and a
scattering medium. We prove the efficiency of the method for extreme learning
machines and reservoir computing to solve a range of challenging learning
tasks, from forecasting chaotic time series to the simultaneous classification
of distinct datasets. Our results open the way for wave-based machine learning
with high energy efficiency, speed, and scalability.Comment: 23 pages, 9 figure
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
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