MemTorch: An open-source simulation framework for memristive deep learning systems
Memristive devices have shown great promise to facilitate the acceleration and improve the
power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using …
power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using …
Neural architecture search for in-memory computing-based deep learning accelerators
O Krestinskaya, ME Fouda, H Benmeziane… - Nature Reviews …, 2024 - nature.com
The rapid growth of artificial intelligence and the increasing complexity of neural network
models are driving demand for efficient hardware architectures that can address power …
models are driving demand for efficient hardware architectures that can address power …
A Software-Circuit-Device Co-Optimization Framework for Neuromorphic Inference Circuits
P Quibuyen, T Jiao, HY Wong - IEEE Access, 2022 - ieeexplore.ieee.org
Neuromorphic circuits, which usually use analog computation for vector-matrix multiplication
(VMM) in neural networks (NN), are promising machine learning accelerators with much …
(VMM) in neural networks (NN), are promising machine learning accelerators with much …
[HTML][HTML] Memristor based object detection using neural network
KI Ravikumar, R Sukumar - High-Confidence Computing, 2022 - Elsevier
With the increasing growth of AI, big data analytics, cloud computing, and Internet of Things
applications, developing memristor devices and related hardware systems to compute the …
applications, developing memristor devices and related hardware systems to compute the …
Long-term accuracy enhancement of binary neural networks based on optimized three-dimensional memristor array
In embedded neuromorphic Internet of Things (IoT) systems, it is critical to improve the
efficiency of neural network (NN) edge devices in inferring a pretrained NN. Meanwhile, in …
efficiency of neural network (NN) edge devices in inferring a pretrained NN. Meanwhile, in …