3,236 research outputs found
Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
Machine learning, particularly in the form of deep learning, has driven most
of the recent fundamental developments in artificial intelligence. Deep
learning is based on computational models that are, to a certain extent,
bio-inspired, as they rely on networks of connected simple computing units
operating in parallel. Deep learning has been successfully applied in areas
such as object/pattern recognition, speech and natural language processing,
self-driving vehicles, intelligent self-diagnostics tools, autonomous robots,
knowledgeable personal assistants, and monitoring. These successes have been
mostly supported by three factors: availability of vast amounts of data,
continuous growth in computing power, and algorithmic innovations. The
approaching demise of Moore's law, and the consequent expected modest
improvements in computing power that can be achieved by scaling, raise the
question of whether the described progress will be slowed or halted due to
hardware limitations. This paper reviews the case for a novel beyond CMOS
hardware technology, memristors, as a potential solution for the implementation
of power-efficient in-memory computing, deep learning accelerators, and spiking
neural networks. Central themes are the reliance on non-von-Neumann computing
architectures and the need for developing tailored learning and inference
algorithms. To argue that lessons from biology can be useful in providing
directions for further progress in artificial intelligence, we briefly discuss
an example based reservoir computing. We conclude the review by speculating on
the big picture view of future neuromorphic and brain-inspired computing
systems.Comment: Keywords: memristor, neuromorphic, AI, deep learning, spiking neural
networks, in-memory computin
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification
Spiking Neural Networks are often touted as brain-inspired learning models
for the third wave of Artificial Intelligence. Although recent SNNs trained
with supervised backpropagation show classification accuracy comparable to deep
networks, the performance of unsupervised learning-based SNNs remains much
lower. This paper presents a heterogeneous recurrent spiking neural network
(HRSNN) with unsupervised learning for spatio-temporal classification of video
activity recognition tasks on RGB (KTH, UCF11, UCF101) and event-based datasets
(DVS128 Gesture). The key novelty of the HRSNN is that the recurrent layer in
HRSNN consists of heterogeneous neurons with varying firing/relaxation
dynamics, and they are trained via heterogeneous
spike-time-dependent-plasticity (STDP) with varying learning dynamics for each
synapse. We show that this novel combination of heterogeneity in architecture
and learning method outperforms current homogeneous spiking neural networks. We
further show that HRSNN can achieve similar performance to state-of-the-art
backpropagation trained supervised SNN, but with less computation (fewer
neurons and sparse connection) and less training data.Comment: 32 pages, 11 Figures, 4 Tables. arXiv admin note: text overlap with
arXiv:1511.03198 by other author
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
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
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