Accurate and energy-efficient bit-slicing for RRAM-based neural networks
Computation-in-memory (CIM) paradigm leverages emerging memory technologies such as
resistive random access memories (RRAMs) to process the data within the memory itself …
resistive random access memories (RRAMs) to process the data within the memory itself …
Artificial synaptic performance with learning behavior for memristor fabricated with stacked solution-processed switching layers
As one of the promising next-generation electronics, brain-inspired synaptic resistive
random access memory (RRAM) devices with stacked solution-processed (SP) spin-coated …
random access memory (RRAM) devices with stacked solution-processed (SP) spin-coated …
Low-power memristor-based computing for edge-ai applications
With the rise of the Internet of Things (IoT), a huge market for so-called smart edge-devices
is foreseen for millions of applications, like personalized healthcare and smart robotics …
is foreseen for millions of applications, like personalized healthcare and smart robotics …
Swordfish: A Framework for Evaluating Deep Neural Network-based Basecalling using Computation-In-Memory with Non-Ideal Memristors
Basecalling, an essential step in many genome analysis studies, relies on large Deep
Neural Network s (DNN s) to achieve high accuracy. Unfortunately, these DNN s are …
Neural Network s (DNN s) to achieve high accuracy. Unfortunately, these DNN s are …
Resistive neural hardware accelerators
K Smagulova, ME Fouda, F Kurdahi… - Proceedings of the …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs), as a subset of machine learning (ML) techniques, entail that
real-world data can be learned, and decisions can be made in real time. However, their wide …
real-world data can be learned, and decisions can be made in real time. However, their wide …
Cost-and dataset-free stuck-at fault mitigation for ReRAM-based deep learning accelerators
G Jung, M Fouda, S Lee, J Lee… - … Design, Automation & …, 2021 - ieeexplore.ieee.org
Resistive RAMs can implement extremely efficient matrix vector multiplication, drawing much
attention for deep learning accelerator research. However, high fault rate is one of the …
attention for deep learning accelerator research. However, high fault rate is one of the …
Threshold learning algorithm for memristive neural network with binary switching behavior
On-chip learning is an effective method for adjusting artificial neural networks in
neuromorphic computing systems by considering hardware intrinsic properties. However, it …
neuromorphic computing systems by considering hardware intrinsic properties. However, it …
Benchmarking inference performance of deep learning models on analog devices
O Fagbohungbe, L Qian - 2021 International Joint Conference …, 2021 - ieeexplore.ieee.org
Analog hardware implemented deep learning models are promising for computation and
energy constrained systems such as edge computing devices. However, the analog nature …
energy constrained systems such as edge computing devices. However, the analog nature …
A fast weight transfer method for real-time online learning in RRAM-based neuromorphic system
In this work, a synaptic weight transfer method for a neuromorphic system based on resistive-
switching random-access memory (RRAM) is proposed and validated. To implement the on …
switching random-access memory (RRAM) is proposed and validated. To implement the on …
Overview of Recent Advancements in Deep Learning and Artificial Intelligence
Artificial intelligence (AI) systems have made significant impact on the society in the recent
years in a wide range of fields, including healthcare, transportation, and finances. In …
years in a wide range of fields, including healthcare, transportation, and finances. In …