Depgraph: Towards any structural pruning
Structural pruning enables model acceleration by removing structurally-grouped parameters
from neural networks. However, the parameter-grouping patterns vary widely across …
from neural networks. However, the parameter-grouping patterns vary widely across …
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 …
Representation and compression of Residual Neural Networks through a multilayer network based approach
In recent years different types of Residual Neural Networks (ResNets, for short) have been
introduced to improve the performance of deep Convolutional Neural Networks. To cope …
introduced to improve the performance of deep Convolutional Neural Networks. To cope …
Teachers do more than teach: Compressing image-to-image models
Abstract Generative Adversarial Networks (GANs) have achieved huge success in
generating high-fidelity images, however, they suffer from low efficiency due to tremendous …
generating high-fidelity images, however, they suffer from low efficiency due to tremendous …
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices
Significant efforts are being invested to bring state-of-the-art classification and recognition to
edge devices with extreme resource constraints (memory, speed, and lack of GPU support) …
edge devices with extreme resource constraints (memory, speed, and lack of GPU support) …
Tiny but accurate: A pruned, quantized and optimized memristor crossbar framework for ultra efficient dnn implementation
The memristor crossbar array has emerged as an intrinsically suitable matrix computation
and low-power acceleration framework for DNN applications. Many techniques such as …
and low-power acceleration framework for DNN applications. Many techniques such as …
An ultra-efficient memristor-based DNN framework with structured weight pruning and quantization using ADMM
The high computation and memory storage of large deep neural networks (DNNs) models
pose intensive challenges to the conventional Von-Neumann architecture, incurring sub …
pose intensive challenges to the conventional Von-Neumann architecture, incurring sub …
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 …
Music genre classification using transfer learning on log-based mel spectrogram
Deep Learning, a branch of Machine Learning is a rapidly expanding field in the Industry 4.0
revolution. The number of applications of Deep Learning are enormous-finding multiple …
revolution. The number of applications of Deep Learning are enormous-finding multiple …
Pruning vs XNOR-Net: A comprehensive study of deep learning for audio classification on edge-devices
Deep learning has celebrated resounding successes in many application areas of relevance
to the Internet of Things (IoT), such as computer vision and machine listening. These …
to the Internet of Things (IoT), such as computer vision and machine listening. These …