no code implementations • 8 Jul 2023 • Olga Krestinskaya, Li Zhang, Khaled Nabil Salama
Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications.
no code implementations • 20 Jun 2020 • Olga Krestinskaya, Bhaskar Choubey, Alex Pappachen James
Generative Adversarial Network (GAN) is a well known computationally complex algorithm requiring signficiant computational resources in software implementations including large amount of data to be trained.
no code implementations • 14 Oct 2019 • Corey Lammie, Olga Krestinskaya, Alex James, Mostafa Rahimi Azghadi
Moreover, we introduce means to mitigate the adverse effect of memristive variations in our proposed networks.
no code implementations • 27 Sep 2018 • Kazybek Adam, Kamilya Smagulova, Olga Krestinskaya, Alex Pappachen James
The automated wafer inspection and quality control is a complex and time-consuming task, which can speed up using neuromorphic memristive architectures, as a separate inspection device or integrating directly into sensors.
no code implementations • 31 Aug 2018 • Olga Krestinskaya, Khaled Nabil Salama, Alex Pappachen James
The circuit level design and implementation of backpropagation algorithm using gradient descent operation for neural network architectures is an open problem.
no code implementations • 2 Aug 2018 • Olga Krestinskaya, Alex Pappachen James
The memristive crossbar aims to implement analog weighted neural network, however, the realistic implementation of such crossbar arrays is not possible due to limited switching states of memristive devices.
no code implementations • 2 Aug 2018 • Olga Krestinskaya, Alex Pappachen James
Probabilistic Neural Network (PNN) is a feed-forward artificial neural network developed for solving classification problems.
no code implementations • 1 Jul 2018 • Olga Krestinskaya, Alex Pappachen James, Leon O. Chua
The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure.
no code implementations • 8 May 2018 • Olga Krestinskaya, Irina Dolzhikova, Alex Pappachen James
This paper presents a survey of the currently available hardware designs for implementation of the human cortex inspired algorithm, Hierarchical Temporal Memory (HTM).
Hardware Architecture Emerging Technologies
no code implementations • 14 Mar 2018 • Olga Krestinskaya, Alex Pappachen James
Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates sparsity, hierarchy and modularity resembling the working principles of neocortex.
no code implementations • 1 Jan 2018 • Olga Krestinskaya, Alex Pappachen James
In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem.
no code implementations • 24 Sep 2017 • Timur Ibrayev, Ulan Myrzakhan, Olga Krestinskaya, Aidana Irmanova, Alex Pappachen James
Hierarchical Temporal Memory is a new machine learning algorithm intended to mimic the working principle of neocortex, part of the human brain, which is responsible for learning, classification, and making predictions.
Emerging Technologies