10,142 research outputs found
Neural Network Methods for Radiation Detectors and Imaging
Recent advances in image data processing through machine learning and
especially deep neural networks (DNNs) allow for new optimization and
performance-enhancement schemes for radiation detectors and imaging hardware
through data-endowed artificial intelligence. We give an overview of data
generation at photon sources, deep learning-based methods for image processing
tasks, and hardware solutions for deep learning acceleration. Most existing
deep learning approaches are trained offline, typically using large amounts of
computational resources. However, once trained, DNNs can achieve fast inference
speeds and can be deployed to edge devices. A new trend is edge computing with
less energy consumption (hundreds of watts or less) and real-time analysis
potential. While popularly used for edge computing, electronic-based hardware
accelerators ranging from general purpose processors such as central processing
units (CPUs) to application-specific integrated circuits (ASICs) are constantly
reaching performance limits in latency, energy consumption, and other physical
constraints. These limits give rise to next-generation analog neuromorhpic
hardware platforms, such as optical neural networks (ONNs), for high parallel,
low latency, and low energy computing to boost deep learning acceleration
Asynchronous spiking neurons, the natural key to exploit temporal sparsity
Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms
Accelerated physical emulation of Bayesian inference in spiking neural networks
The massively parallel nature of biological information processing plays an
important role for its superiority to human-engineered computing devices. In
particular, it may hold the key to overcoming the von Neumann bottleneck that
limits contemporary computer architectures. Physical-model neuromorphic devices
seek to replicate not only this inherent parallelism, but also aspects of its
microscopic dynamics in analog circuits emulating neurons and synapses.
However, these machines require network models that are not only adept at
solving particular tasks, but that can also cope with the inherent
imperfections of analog substrates. We present a spiking network model that
performs Bayesian inference through sampling on the BrainScaleS neuromorphic
platform, where we use it for generative and discriminative computations on
visual data. By illustrating its functionality on this platform, we implicitly
demonstrate its robustness to various substrate-specific distortive effects, as
well as its accelerated capability for computation. These results showcase the
advantages of brain-inspired physical computation and provide important
building blocks for large-scale neuromorphic applications.Comment: This preprint has been published 2019 November 14. Please cite as:
Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian
Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi:
10.3389/fnins.2019.0120
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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