Federated learning for computationally constrained heterogeneous devices: A survey
With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …
field, offloading training of neural networks (NNs) to a central server becomes more and …
A comprehensive survey on model quantization for deep neural networks in image classification
Recent advancements in machine learning achieved by Deep Neural Networks (DNNs)
have been significant. While demonstrating high accuracy, DNNs are associated with a …
have been significant. While demonstrating high accuracy, DNNs are associated with a …
Full stack optimization of transformer inference: a survey
Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …
Transformer models. These models achieve superior accuracy across a wide range of …
[HTML][HTML] A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction
Predicting electricity demand data is considered an essential task in decisions taking, and
establishing new infrastructure in the power generation network. To deliver a high-quality …
establishing new infrastructure in the power generation network. To deliver a high-quality …
Advancements in accelerating deep neural network inference on aiot devices: A survey
The amalgamation of artificial intelligence with Internet of Things (AIoT) devices have seen a
rapid surge in growth, largely due to the effective implementation of deep neural network …
rapid surge in growth, largely due to the effective implementation of deep neural network …
Computational complexity optimization of neural network-based equalizers in digital signal processing: a comprehensive approach
P Freire, S Srivallapanondh, B Spinnler… - Journal of Lightwave …, 2024 - ieeexplore.ieee.org
Experimental results based on offline processing reported at optical conferences
increasingly rely on neural network-based equalizers for accurate data recovery. However …
increasingly rely on neural network-based equalizers for accurate data recovery. However …
Adapt: Fast emulation of approximate dnn accelerators in pytorch
Current state-of-the-art employs approximate multipliers to address the highly increased
power demands of deep neural network (DNN) accelerators. However, evaluating the …
power demands of deep neural network (DNN) accelerators. However, evaluating the …
A survey of FPGA-based vision systems for autonomous cars
On the road to making self-driving cars a reality, academic and industrial researchers are
working hard to continue to increase safety while meeting technical and regulatory …
working hard to continue to increase safety while meeting technical and regulatory …
Special session: Approximation and fault resiliency of dnn accelerators
MH Ahmadilivani, M Barbareschi… - 2023 IEEE 41st VLSI …, 2023 - ieeexplore.ieee.org
Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in
many scenarios, including safety-critical applications such as autonomous driving. In this …
many scenarios, including safety-critical applications such as autonomous driving. In this …
Stacked NbOx-based selector and ZrOx-based resistive memory for high-density crossbar array applications
Resistive random-access memory (RRAM) is a promising candidate for next-generation
nonvolatile memory (NVM). Furthermore, RRAM is highly suitable for integration as a …
nonvolatile memory (NVM). Furthermore, RRAM is highly suitable for integration as a …