AZ
Amir Zjajo
Contributed
9 records found
N-shot Training Methodology
For Spiking Neural Networks(SNNs)
Traditional Artificial Neural Networks(ANNs)like CNNs have shown tremendous opportunities in various domains like autonomous cars, disease diagnosis, etc. Proven learning algorithms like backpropagation help ANNs in achieving higher accuracy. But there is a serious challenge with
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Population Step Forward Encoding Algorithm
Improving the signal encoding accuracy and efficiency of spike encoding algorithms
Conversion from digital information to spike trains is needed for Spiking Neural Networks. Moreover, it is one of the most important steps for Spiking Neural Networks. This conversion could lead to much information loss depending on which encoding algorithm is used. Another major
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Physical Characterization of Asynchronous Logic Library
A Design of AER Transmitter and Its Characterization and Back-end Design Flow
Neuromorphic electronic systems have used asynchronous logic combined with continuous-time analog circuits to emulate neurons, synapses, and learning algorithms. It is attractive because of its low power consumption and feasible implementation. Typically, the neuron firing rates
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A robust modular spiking neural networks training methodology for time-series datasets
With a focus on gesture control
Neurons in Spiking Neural Networks (SNNs) communicate through spikes, similarly that neurons in the brain communicate, thus mimicking the brain. The working of SNNs is temporally based, as the spikes are time-dependent. SNNs have the benefit to perform continual classification, a
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Spiking neural networks (SNN), as the third-generation artificial neural network, has a similar potential pulse triggering mechanism to the biological neuron. This mechanism enables the spiking neural network to increase computing power compared to the traditional artificial neur
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Mobile devices are getting increasingly powerful, becoming compatible
for an ever increasing set of functionality. Applications based around
neural networks however still have to offload parts of their computations
to the cloud since current Artificial Neural Networks ...
for an ever increasing set of functionality. Applications based around
neural networks however still have to offload parts of their computations
to the cloud since current Artificial Neural Networks ...
The development of the Spiking Neural Network (SNN) offers great potential in combination with new types of event-based sensors, by exploiting the embedded temporal information. When combined with dedicated neuromorphic hardware it enables ultra-low power solutions and local on-c
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This dissertation describes an approach to building a self-timed asynchronous pulse-mode serial link circuit. Unlike asynchronous handshake circuits or synchronous circuits, this design style does not require any feedback control blocks, which can increase latency, or any clock r
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Autonomous vehicle (AV technology) relies heavily on vision based applications like object recognition, obstacle/collision avoidance etc. In order to achieve this, understanding and estimating the dynamics in the environment is extremely important. LIDARs are proven to detect bot
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