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The performance of automatic speech recognition systems under noisy environments still leaves room for improvement. Speech enhancement or feature enhancement techniques for increasing noise robustness of these systems usually add components to the recognition system that need careful optimization. In this work, we propose the use of a relatively simple curriculum training strategy called accordion...
We previously described a deep network system that reached an accuracy of 82% on a digit recognition task using the spike outputs from a Dynamic Audio Sensor (DAS) in response to audio samples from the TIDIGITS database. The audio samples were played directly to the system therefore bypassing the microphones. This work presents an interactive real-time demonstration of this digit recognition system...
This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor “frames” that consist of a constant...
A big challenge of reservoir-based Recurrent Neural Networks (RNNs) is the optimization of the connection weights within the network so that the network performance is optimal for the intended task of temporal sequence recognition. One particular RNN called the Self-Organizing Recurrent Network (SORN) avoids the mathematical normalization required after each initialization. Instead, three types of...
We demonstrate an interactive digit recognition system using a spiking Deep Neural Network (DNN) FPGA-based system connected to two event-driven sensors: a Dynamic Vision Sensor (DVS) and a Dynamic Audio Sensor (DAS). Sensor fusion is demonstrated on a digit classification task using a DNN trained on the MNIST dataset supplemented by assignment of a unique pure ton e for each digit.
The use of spiking neuromorphic sensors with state-of-art deep networks is currently an active area of research. Still relatively unexplored are the pre-processing steps needed to transform spikes from these sensors and the types of network architectures that can produce high-accuracy performance using these sensors. This paper discusses several methods for preprocessing the spiking data from these...
This paper reports an object tracking algorithm for a moving platform using the dynamic and active-pixel vision sensor (DAVIS). It takes advantage of both the active pixel sensor (APS) frame and dynamic vision sensor (DVS) event outputs from the DAVIS. The tracking is performed in a three step-manner: regions of interest (ROIs) are generated by a cluster-based tracking using the DVS output, likely...
This paper presents a real-time multi-modal spiking Deep Neural Network (DNN) implemented on an FPGA platform. The hardware DNN system, called n-Minitaur, demonstrates a 4-fold improvement in computational speed over the previous DNN FPGA system. The proposed system directly interfaces two different event-based sensors: a Dynamic Vision Sensor (DVS) and a Dynamic Audio Sensor (DAS). The DNN for this...
Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief Networks (DBNs) represent the state-of-the-art for many machine learning and computer vision classification problems. To overcome the large computational cost of deep networks, spiking deep networks have recently been proposed, given the specialized hardware now available for spiking neural networks (SNNs). However, this...
Deep neural networks have become the state-of-the-art approach for classification in machine learning, and Deep Belief Networks (DBNs) are one of its most successful representatives. DBNs consist of many neuron-like units, which are connected only to neurons in neighboring layers. Larger DBNs have been shown to perform better, but scaling-up poses problems for conventional CPUs, which calls for efficient...
We demonstrate an interactive handwritten digit recognition system with a spike-based deep belief network running in real-time on SpiNNaker, a biologically inspired many-core architecture. Results show that during the simulation a SpiNNaker chip can deliver spikes in under 1 μs, with a classification latency in the order of tens of milliseconds, while consuming less than 0.3 W.
Current neural networks are accumulating accolades for their performance on a variety of real-world computational tasks including recognition, classification, regression, and prediction, yet there are few scalable architectures that have emerged to address the challenges posed by their computation. This paper introduces Minitaur, an event-driven neural network accelerator, which is designed for low...
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