324 citations · 605 across the 28 of their papers we have counts for
31 papers
Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators
Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil +1
Software-hardware co-design is essential for optimizing in-memory computing (IMC) hardware accelerators for neural networks. However, most existing optimization frameworks target a…
Mixed-Precision Quantization for Language Models: Techniques and Prospects
Mariam Rakka, Marios Fournarakis, Olga Krestinskaya +5
The rapid scaling of language models (LMs) has resulted in unprecedented computational, memory, and energy requirements, making their training and deployment increasingly unsustain…
CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search
Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil +1
To maximize hardware efficiency and performance accuracy in Compute-In-Memory (CIM)-based neural network accelerators for Artificial Intelligence (AI) applications, co-optimizing b…
Towards Efficient IMC Accelerator Design Through Joint Hardware-Workload Co-optimization
Olga Krestinskaya, Mohammed E. Fouda, Ahmed Eltawil +1
Designing generalized in-memory computing (IMC) hardware that efficiently supports a variety of workloads requires extensive design space exploration, which is infeasible to perfor…
Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives
Olga Krestinskaya, Li Zhang, Khaled Nabil Salama
The amount of data processed in the cloud, the development of Internet-of-Things (IoT) applications, and growing data privacy concerns force the transition from cloud-based to edge…
Towards Efficient RRAM-based Quantized Neural Networks Hardware: State-of-the-art and Open Issues
O. Krestinskaya, L. Zhang, K. N. Salama
The increasing amount of data processed on edge and the demand for reducing the energy consumption for large neural network architectures have initiated the transition from traditi…
Analog Image Denoising with an Adaptive Memristive Crossbar Network
O. Krestinskaya, K. N. Salama, A. P. James
Noise in image sensors led to the development of a whole range of denoising filters. A noisy image can become hard to recognize and often require several types of post-processing c…
AM-DCGAN: Analog Memristive Hardware Accelerator for Deep Convolutional Generative Adversarial Networks
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 amo…
Variation-aware Binarized Memristive Networks
Corey Lammie, Olga Krestinskaya, Alex James +1
The quantization of weights to binary states in Deep Neural Networks (DNNs) can replace resource-hungry multiply accumulate operations with simple accumulations. Such Binarized Neu…
Wafer Quality Inspection using Memristive LSTM, ANN, DNN and HTM
Kazybek Adam, Kamilya Smagulova, Olga Krestinskaya +1
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 de…
Programmable Memristive Threshold Logic Gate Array
Olga Krestinskaya, Akshay Kumar Maan, Alex Pappachen James
This paper proposes the implementation of programmable threshold logic gate (TLG) crossbar array based on modified TLG cells for high speed processing and computation. The proposed…
Learning in Memristive Neural Network Architectures using Analog Backpropagation Circuits
Olga Krestinskaya, Khaled Nabil Salama, Alex Pappachen James
The on-chip implementation of learning algorithms would speed-up the training of neural networks in crossbar arrays. The circuit level design and implementation of backpropagation…
Real-time Analog Pixel-to-pixel Dynamic Frame Differencing with Memristive Sensing Circuits
Olga Krestinskaya, Alex Pappachen James
In this paper, we propose an analog pixel differencing circuit for differentiating pixels between frames directly from CMOS pixels. The analog information processing at sensor is a…
Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing
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 sta…
Approximate Probabilistic Neural Networks with Gated Threshold Logic
Olga Krestinskaya, Alex Pappachen James
Probabilistic Neural Network (PNN) is a feed-forward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an ap…
Neuro-memristive Circuits for Edge Computing: A review
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…
Design of CMOS-memristor Circuits for LSTM architecture
Kamilya Smagulova, Kazybek Adam, Olga Krestinskaya +1
Long Short-Term memory (LSTM) architecture is a well-known approach for building recurrent neural networks (RNN) useful in sequential processing of data in application to natural l…
Effects of Memristors on Fully Differential Transimpedance Amplifier Performance
Berik Argimbayev, Olga Krestinskaya, Alex Pappachen James
The progress of the Internet of Things(IoT) technologies and applications requires the efficient low power circuits and architectures to maintain and improve the performance of the…
Hierarchical Temporal Memory using Memristor Networks: A Survey
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). In this revi…
CMOS-Memristive Analog Multiplier Design
Ileskhan Kalysh, Olga Krestinskaya, Alex Pappachen James
This paper proposes four quadrant analog multiplier using CMOS-memristor circuit. Currently, there are plenty of analog multipliers using resistors and CMOS transistors. They can a…
Analysis of Multilayer Perceptron with Rectifier Linear Unit Activation Function
Meirambek Mukhametkhan, Olga Krestinskaya, Alex Pappachen James
The implementation of analog neural network and online analog learning circuits based on memristive crossbar has been intensively explored in recent years. The implementation of va…
Analog multiplier design with CMOS-memristor circuits
Aidos Kanapyanov, Olga Krestinskaya
CMOS-transistors circuits have been used as a conventional approach for designing an analog multiplier in modern era of industrial electronics. However, previous studies have shown…
Variability analysis of Memristor-based Sigmoid Function
Nursultan Kaiyrbekov, Olga Krestinskaya, Alex Pappachen James
Activation functions are widely used in neural networks to decide the activation value of the neural unit based upon linear combinations of the weighted inputs. The effective imple…
Perceptron Linear Function Design with CMOS-Memristive Circuits
Bexultan Nursultan, Olga Krestinskaya
In the last decade, the interest to emulation of the functionality and structure of the human brain to solve the problems related to image processing and pattern recognition, espec…