Hardware implementation of memristor-based artificial neural networks
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …
techniques, which rely on networks of connected simple computing units operating in …
Spvit: Enabling faster vision transformers via latency-aware soft token pruning
Abstract Recently, Vision Transformer (ViT) has continuously established new milestones in
the computer vision field, while the high computation and memory cost makes its …
the computer vision field, while the high computation and memory cost makes its …
Yolobile: Real-time object detection on mobile devices via compression-compilation co-design
The rapid development and wide utilization of object detection techniques have aroused
attention on both accuracy and speed of object detectors. However, the current state-of-the …
attention on both accuracy and speed of object detectors. However, the current state-of-the …
[HTML][HTML] Modeling and simulating in-memory memristive deep learning systems: An overview of current efforts
Deep Learning (DL) systems have demonstrated unparalleled performance in many
challenging engineering applications. As the complexity of these systems inevitably …
challenging engineering applications. As the complexity of these systems inevitably …
Forms: Fine-grained polarized reram-based in-situ computation for mixed-signal dnn accelerator
Recent work demonstrated the promise of using resistive random access memory (ReRAM)
as an emerging technology to perform inherently parallel analog domain in-situ matrix …
as an emerging technology to perform inherently parallel analog domain in-situ matrix …
Achieving on-mobile real-time super-resolution with neural architecture and pruning search
Though recent years have witnessed remarkable progress in single image super-resolution
(SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep …
(SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep …
Structured pruning of RRAM crossbars for efficient in-memory computing acceleration of deep neural networks
The high computational complexity and a large number of parameters of deep neural
networks (DNNs) become the most intensive burden of deep learning hardware design …
networks (DNNs) become the most intensive burden of deep learning hardware design …
Improving dnn fault tolerance using weight pruning and differential crossbar mapping for reram-based edge ai
Recent research demonstrated the promise of using resistive random access memory
(ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ …
(ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ …
Memtorch: A simulation framework for deep memristive cross-bar architectures
C Lammie, MR Azghadi - 2020 IEEE international symposium …, 2020 - ieeexplore.ieee.org
Memristive devices arranged in cross-bar architectures have shown great promise to
facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems …
facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems …
Tinyadc: Peripheral circuit-aware weight pruning framework for mixed-signal dnn accelerators
As the number of weight parameters in deep neural networks (DNNs) continues growing, the
demand for ultra-efficient DNN accelerators has motivated research on non-traditional …
demand for ultra-efficient DNN accelerators has motivated research on non-traditional …