3,165 research outputs found
Joint Device-Edge Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization
Semantic communication, an intelligent communication paradigm that aims to
transmit useful information in the semantic domain, is facilitated by deep
learning techniques. Although robust semantic features can be learned and
transmitted in an analog fashion, it poses new challenges to hardware,
protocol, and encryption. In this paper, we propose a digital semantic
communication system, which consists of an encoding network deployed on a
resource-limited device and a decoding network deployed at the edge. To acquire
better semantic representation for digital transmission, a novel non-linear
quantization module is proposed with the trainable quantization levels that
efficiently quantifies semantic features. Additionally, structured pruning by a
sparse scaling vector is incorporated to reduce the dimension of the
transmitted features. We also introduce a semantic learning loss (SLL) function
to reduce semantic error. To adapt to various channel conditions and inputs
under constraints of communication and computing resources, a policy network is
designed to adaptively choose the split point and the dimension of the
transmitted semantic features. Experiments using the CIFAR-10 dataset for image
classification are employed to evaluate the proposed digital semantic
communication network, and ablation studies are conducted to assess the
proposed modules including the quantization module, structured pruning and SLL
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
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