2,819 research outputs found
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
Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
Recent advances in Voice Activity Detection (VAD) are driven by artificial
and Recurrent Neural Networks (RNNs), however, using a VAD system in
battery-operated devices requires further power efficiency. This can be
achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs)
to perform inference at very low energy consumption. Spiking networks are
characterized by their ability to process information efficiently, in a sparse
cascade of binary events in time called spikes. However, a big performance gap
separates artificial from spiking networks, mostly due to a lack of powerful
SNN training algorithms. To overcome this problem we exploit an SNN model that
can be recast into an RNN-like model and trained with known deep learning
techniques. We describe an SNN training procedure that achieves low spiking
activity and pruning algorithms to remove 85% of the network connections with
no performance loss. The model achieves state-of-the-art performance with a
fraction of power consumption comparing to other methods.Comment: 5 pages, 2 figures, 2 table
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Over the past decade, deep neural networks (DNNs) have demonstrated
remarkable performance in a variety of applications. As we try to solve more
advanced problems, increasing demands for computing and power resources has
become inevitable. Spiking neural networks (SNNs) have attracted widespread
interest as the third-generation of neural networks due to their event-driven
and low-powered nature. SNNs, however, are difficult to train, mainly owing to
their complex dynamics of neurons and non-differentiable spike operations.
Furthermore, their applications have been limited to relatively simple tasks
such as image classification. In this study, we investigate the performance
degradation of SNNs in a more challenging regression problem (i.e., object
detection). Through our in-depth analysis, we introduce two novel methods:
channel-wise normalization and signed neuron with imbalanced threshold, both of
which provide fast and accurate information transmission for deep SNNs.
Consequently, we present a first spiked-based object detection model, called
Spiking-YOLO. Our experiments show that Spiking-YOLO achieves remarkable
results that are comparable (up to 98%) to those of Tiny YOLO on non-trivial
datasets, PASCAL VOC and MS COCO. Furthermore, Spiking-YOLO on a neuromorphic
chip consumes approximately 280 times less energy than Tiny YOLO and converges
2.3 to 4 times faster than previous SNN conversion methods.Comment: Accepted to AAAI 202
Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks
We study the collective dynamics of a Leaky Integrate and Fire network in
which precise relative phase relationship of spikes among neurons are stored,
as attractors of the dynamics, and selectively replayed at differentctime
scales. Using an STDP-based learning process, we store in the connectivity
several phase-coded spike patterns, and we find that, depending on the
excitability of the network, different working regimes are possible, with
transient or persistent replay activity induced by a brief signal. We introduce
an order parameter to evaluate the similarity between stored and recalled
phase-coded pattern, and measure the storage capacity. Modulation of spiking
thresholds during replay changes the frequency of the collective oscillation or
the number of spikes per cycle, keeping preserved the phases relationship. This
allows a coding scheme in which phase, rate and frequency are dissociable.
Robustness with respect to noise and heterogeneity of neurons parameters is
studied, showing that, since dynamics is a retrieval process, neurons preserve
stablecprecise phase relationship among units, keeping a unique frequency of
oscillation, even in noisy conditions and with heterogeneity of internal
parameters of the units
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