no code implementations • 23 Nov 2023 • Vishal Saxena, Md Jubayer Shawon
Compact modeling of static and transient dynamics of these modulators is important for co-simulation with CMOS drivers and wavelength stabilization circuits.
no code implementations • 3 May 2020 • Ruthvik Vaila, John Chiasson, Vishal Saxena
Deep Neural Networks (DNNs) have two key deficiencies, their dependence on high precision computing and their inability to perform sequential learning, that is, when a DNN is trained on a first task and the same DNN is trained on the next task it forgets the first task.
1 code implementation • 26 Feb 2020 • Ruthvik Vaila, John Chiasson, Vishal Saxena
The effect of stochastic gradient descent (SGD) approximations on learning capabilities of our network are also explored.
no code implementations • 28 Mar 2019 • Ruthvik Vaila, John Chiasson, Vishal Saxena
Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing dependant plasticity.
no code implementations • 7 Feb 2018 • Vishal Saxena, Xinyu Wu, Kehan Zhu
Emerging non-volatile memory (NVM), or memristive, devices promise energy-efficient realization of deep learning, when efficiently integrated with mixed-signal integrated circuits on a CMOS substrate.
no code implementations • 9 Jan 2018 • Xinyu Wu, Vishal Saxena
Brain-inspired learning mechanisms, e. g. spike timing dependent plasticity (STDP), enable agile and fast on-the-fly adaptation capability in a spiking neural network.
no code implementations • 5 Dec 2016 • Xinyu Wu, Vishal Saxena
Large-scale integration of emerging nanoscale non-volatile memory devices, e. g. resistive random-access memory (RRAM), can enable a new generation of neuromorphic computers that can solve a wide range of machine learning problems.
no code implementations • 2 Jun 2015 • Xinyu Wu, Vishal Saxena, Kehan Zhu
Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing.
no code implementations • 2 Jun 2015 • Xinyu Wu, Vishal Saxena, Kehan Zhu
A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density.
no code implementations • 28 May 2015 • Xinyu Wu, Vishal Saxena, Kehan Zhu, Sakkarapani Balagopal
Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system.