6,723 research outputs found
Local learning algorithms for stochastic spiking neural networks
This dissertation focuses on the development of machine learning algorithms for spiking neural networks, with an emphasis on local three-factor learning rules that are in keeping with the constraints imposed by current neuromorphic hardware. Spiking neural networks (SNNs) are an alternative to artificial neural networks (ANNs) that follow a similar graphical structure but use a processing paradigm more closely modeled after the biological brain in an effort to harness its low power processing capability. SNNs use an event based processing scheme which leads to significant power savings when implemented in dedicated neuromorphic hardware such as Intel’s Loihi chip.
This work is distinguished by the consideration of stochastic SNNs based on spiking neurons that employ a stochastic spiking process, implementing generalized linear models (GLM) rather than deterministic thresholded spiking. In this framework, the spiking signals are random variables which may be sampled from a distribution defined by the neurons. The spiking signals may be observed or latent variables, with neurons whose outputs are observed termed visible neurons and otherwise termed hidden neurons. This choice provides a strong mathematical basis for maximum likelihood optimization of the network parameters via stochastic gradient descent, avoiding the issue of gradient backpropagation through the discontinuity created by the spiking process.
Three machine learning algorithms are developed for stochastic SNNs with a focus on power efficiency, learning efficiency and model adaptability; characteristics that are valuable in resource constrained settings. They are studied in the context of applications where low power learning on the edge is key. All of the learning rules that are derived include only local variables along with a global learning signal, making these algorithms tractable to implementation in current neuromorphic hardware.
First, a stochastic SNN that includes only visible neurons, the simplest case for probabilistic optimization, is considered. A policy gradient reinforcement learning (RL) algorithm is developed in which the stochastic SNN defines the policy, or state-action distribution, of an RL agent. Action choices are sampled directly from the policy by interpreting the outputs of the read-out neurons using a first to spike decision rule. This study highlights the power efficiency of the SNN in terms of spike frequency.
Next, an online meta-learning framework is proposed with the goal of progressively improving the learning efficiency of an SNN over a stream of tasks. In this setting, SNNs including both hidden and visible neurons are considered, posing a more complex maximum likelihood learning problem that is solved using a variational learning method. The meta-learning rule yields a hyperparameter initialization for SNN models that supports fast adaptation of the model to individualized data on edge devices.
Finally, moving away from the supervised learning paradigm, a hybrid adver-sarial training framework for SNNs, termed SpikeGAN, is developed. Rather than optimize for the likelihood of target spike patterns at the SNN outputs, the training is mediated by an auxiliary discriminator that provides a measure of how similar the spiking data is to a target distribution. Because no direct spiking patterns are given, the SNNs considered in adversarial learning include only hidden neurons. A Bayesian adaptation of the SpikeGAN learning rule is developed to broaden the range of temporal data that a single SpikeGAN can estimate. Additionally, the online meta-learning rule is extended to include meta-learning for SpikeGAN, to enable efficient generation of data from sequential data distributions
Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model
A fundamental challenge in machine learning today is to build a model that
can learn from few examples. Here, we describe a reservoir based spiking neural
model for learning to recognize actions with a limited number of labeled
videos. First, we propose a novel encoding, inspired by how microsaccades
influence visual perception, to extract spike information from raw video data
while preserving the temporal correlation across different frames. Using this
encoding, we show that the reservoir generalizes its rich dynamical activity
toward signature action/movements enabling it to learn from few training
examples. We evaluate our approach on the UCF-101 dataset. Our experiments
demonstrate that our proposed reservoir achieves 81.3%/87% Top-1/Top-5
accuracy, respectively, on the 101-class data while requiring just 8 video
examples per class for training. Our results establish a new benchmark for
action recognition from limited video examples for spiking neural models while
yielding competetive accuracy with respect to state-of-the-art non-spiking
neural models.Comment: 13 figures (includes supplementary information
Towards Accurate and High-Speed Spiking Neuromorphic Systems with Data Quantization-Aware Deep Networks
Deep Neural Networks (DNNs) have gained immense success in cognitive
applications and greatly pushed today's artificial intelligence forward. The
biggest challenge in executing DNNs is their extremely data-extensive
computations. The computing efficiency in speed and energy is constrained when
traditional computing platforms are employed in such computational hungry
executions. Spiking neuromorphic computing (SNC) has been widely investigated
in deep networks implementation own to their high efficiency in computation and
communication. However, weights and signals of DNNs are required to be
quantized when deploying the DNNs on the SNC, which results in unacceptable
accuracy loss. %However, the system accuracy is limited by quantizing data
directly in deep networks deployment. Previous works mainly focus on weights
discretize while inter-layer signals are mainly neglected. In this work, we
propose to represent DNNs with fixed integer inter-layer signals and
fixed-point weights while holding good accuracy. We implement the proposed DNNs
on the memristor-based SNC system as a deployment example. With 4-bit data
representation, our results show that the accuracy loss can be controlled
within 0.02% (2.3%) on MNIST (CIFAR-10). Compared with the 8-bit dynamic
fixed-point DNNs, our system can achieve more than 9.8x speedup, 89.1% energy
saving, and 30% area saving.Comment: 6 pages, 4 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
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus eliminated most of the density advantages gained by the adoption of
memristor synapses. Further, they used different waveforms for pre and
post-synaptic spikes that added undesirable circuit overhead. Here we describe
a hardware architecture that can feature a large number of memristor synapses
to learn real-world patterns. We present a versatile CMOS neuron that combines
integrate-and-fire behavior, drives passive memristors and implements
competitive learning in a compact circuit module, and enables in-situ
plasticity in the memristor synapses. We demonstrate handwritten-digits
recognition using the proposed architecture using transistor-level circuit
simulations. As the described neuromorphic architecture is homogeneous, it
realizes a fundamental building block for large-scale energy-efficient
brain-inspired silicon chips that could lead to next-generation cognitive
computing.Comment: This is a preprint of an article accepted for publication in IEEE
Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no.
2, June 201
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Spiking neural networks (SNN) are artificial computational models that have
been inspired by the brain's ability to naturally encode and process
information in the time domain. The added temporal dimension is believed to
render them more computationally efficient than the conventional artificial
neural networks, though their full computational capabilities are yet to be
explored. Recently, computational memory architectures based on non-volatile
memory crossbar arrays have shown great promise to implement parallel
computations in artificial and spiking neural networks. In this work, we
experimentally demonstrate for the first time, the feasibility to realize
high-performance event-driven in-situ supervised learning systems using
nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize
audio signals of alphabets encoded using spikes in the time domain and to
generate spike trains at precise time instances to represent the pixel
intensities of their corresponding images. Moreover, with a statistical model
capturing the experimental behavior of the devices, we investigate
architectural and systems-level solutions for improving the training and
inference performance of our computational memory-based system. Combining the
computational potential of supervised SNNs with the parallel compute power of
computational memory, the work paves the way for next-generation of efficient
brain-inspired systems
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