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A survey on efficient convolutional neural networks and hardware acceleration
Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …
performance in academia and industry. The learning capability of convolutional neural …
Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots
S Ma, J Pei, W Zhang, G Wang, D Feng, F Yu… - Science Robotics, 2022 - science.org
Recent advances in artificial intelligence have enhanced the abilities of mobile robots in
dealing with complex and dynamic scenarios. However, to enable computationally intensive …
dealing with complex and dynamic scenarios. However, to enable computationally intensive …
An efficient unstructured sparse convolutional neural network accelerator for wearable ECG classification device
J Lu, D Liu, X Cheng, L Wei, A Hu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Convolution neural network (CNN) with pruning techniques has shown remarkable
prospects in electrocardiogram (ECG) classification. However, efficiently deploying the …
prospects in electrocardiogram (ECG) classification. However, efficiently deploying the …
Hardware accelerator design for sparse DNN inference and training: A tutorial
W Mao, M Wang, X **-pong weight update
Computing-in-memory (CIM) chips have demonstrated the potential high energy efficiency
for low-power neural network (NN) processors. Even with energy-efficient CIM macros, the …
for low-power neural network (NN) processors. Even with energy-efficient CIM macros, the …
HD-CIM: Hybrid-device computing-in-memory structure based on MRAM and SRAM to reduce weight loading energy of neural networks
SRAM based computing-in-memory (SRAM-CIM) techniques have been widely studied for
neural networks (NNs) to solve the “Von Neumann bottleneck”. However, as the scale of the …
neural networks (NNs) to solve the “Von Neumann bottleneck”. However, as the scale of the …
A 28nm 2D/3D unified sparse convolution accelerator with block-wise neighbor searcher for large-scaled voxel-based point cloud network
3D processing plays an important role in many emerging applications such as autonomous
driving, visual navigation and virtual reality. Recent research shows that adopting 3D voxel …
driving, visual navigation and virtual reality. Recent research shows that adopting 3D voxel …
ETA: An efficient training accelerator for DNNs based on hardware-algorithm co-optimization
Recently, the efficient training of deep neural networks (DNNs) on resource-constrained
platforms has attracted increasing attention for protecting user privacy. However, it is still a …
platforms has attracted increasing attention for protecting user privacy. However, it is still a …
Demystifying map space exploration for npus
Map Space Exploration is the problem of finding optimized map**s of a Deep Neural
Network (DNN) model on an accelerator. It is known to be extremely computationally …
Network (DNN) model on an accelerator. It is known to be extremely computationally …