|
|
|
|
|
Roman Rybka, Yury Davydov, Danila Vlasov, Alexey Serenko, Alexander Sboev and Vyacheslav Ilyin
Developing a spiking neural network architecture that could prospectively be trained on energy-efficient neuromorphic hardware to solve various data analysis tasks requires satisfying the limitations of prospective analog or digital hardware, i.e., local...
ver más
|
|
|
|
|
|
|
|
|
|
|
Alexander Sboev, Roman Rybka, Dmitry Kunitsyn, Alexey Serenko, Vyacheslav Ilyin and Vadim Putrolaynen
In this paper, we demonstrate that fixed-weight layers generated from random distribution or logistic functions can effectively extract significant features from input data, resulting in high accuracy on a variety of tasks, including Fisher?s Iris, Wisco...
ver más
|
|
|
|
|
|
|
|
|
|
|
John S. Venker, Luke Vincent and Jeff Dix
A Spiking Neural Network (SNN) is realized within a 65 nm CMOS process to demonstrate the feasibility of its constituent cells. Analog hardware neural networks have shown improved energy efficiency in edge computing for real-time-inference applications, ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Arash Khajooei, Mohammad (Behdad) Jamshidi and Shahriar B. Shokouhi
Although the Metaverse is becoming a popular technology in many aspects of our lives, there are some drawbacks to its implementation on clouds, including long latency, security concerns, and centralized infrastructures. Therefore, designing scalable Meta...
ver más
|
|
|
|
|
|
|
|
|
|
|
Sergey Shchanikov, Ilya Bordanov, Alexey Kucherik, Evgeny Gryaznov and Alexey Mikhaylov
Arrays of memristive devices coupled with photosensors can be used for capturing and processing visual information, thereby realizing the concept of ?in-sensor computing?. This is a promising concept associated with the development of compact and low-pow...
ver más
|
|
|
|
|
|
|
|
|
|
|
Huynh Cong Viet Ngu and Keon Myung Lee
Due to energy efficiency, spiking neural networks (SNNs) have gradually been considered as an alternative to convolutional neural networks (CNNs) in various machine learning tasks. In image recognition tasks, leveraging the superior capability of CNNs, t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Krishnamurthy V. Vemuru
Edge detectors are widely used in computer vision applications to locate sharp intensity changes and find object boundaries in an image. The Canny edge detector is the most popular edge detector, and it uses a multi-step process, including the first step...
ver más
|
|
|
|
|
|
|
|
|
|
|
Octavio Delgadillo, Bernhard Blieninger, Juri Kuhn and Uwe Baumgarten
Consolidating tasks to a smaller number of electronic control units (ECUs) is an important strategy for optimizing costs and resources in the automotive industry. In our research, we aim to enable ECU consolidation by migrating tasks at runtime between d...
ver más
|
|
|
|
|
|
|
|
|
|
|
Taylor Barton, Hao Yu, Kyle Rogers, Nancy Fulda, Shiuh-hua Wood Chiang, Jordan Yorgason and Karl F. Warnick
We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constant an...
ver más
|
|
|
|
|
|
|
|
|
|
|
Robert Kleijnen, Markus Robens, Michael Schiek and Stefan van Waasen
Accelerated simulations of biological neural networks are in demand to discover the principals of biological learning. Novel many-core simulation platforms, e.g., SpiNNaker, BrainScaleS and Neurogrid, allow one to study neuron behavior in the brain at an...
ver más
|
|
|
|
|
|