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09/21/2023
SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices
Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with...
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11/22/2022
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors
As data become increasingly vital for deep learning, a company would be ...
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11/02/2022
The Lottery Ticket Hypothesis for Vision Transformers
The conventional lottery ticket hypothesis (LTH) claims that there exist...
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10/06/2022
DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices
Data compression has been widely adopted to release mobile devices from ...
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09/22/2022
Layer Freezing Data Sieving: Missing Pieces of a Generic Framework for Sparse Training
Recently, sparse training has emerged as a promising paradigm for effici...
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06/02/2022
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization
Neural architecture search (NAS) and network pruning are widely studied ...
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06/02/2022
EfficientFormer: Vision Transformers at MobileNet Speed
Vision Transformers (ViT) have shown rapid progress in computer vision t...
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07/01/2021
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
There have been long-standing controversies and inconsistencies over the...
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06/16/2021
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 acces...
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06/16/2021
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
Recent works demonstrated the promise of using resistive random access m...
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03/05/2021
Teachers Do More Than Teach: Compressing Image-to-Image Models
Generative Adversarial Networks (GANs) have achieved huge success in gen...
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02/19/2021
Lottery Ticket Implies Accuracy Degradation, Is It a Desirable Phenomenon?
In deep model compression, the recent finding "Lottery Ticket Hypothesis...
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01/23/2020
SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency
Structured weight pruning is a representative model compression techniqu...
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11/24/2019
A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation
The computing wall and data movement challenges of deep neural networks ...
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08/29/2019
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 (D...
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08/27/2019
Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation
The state-of-art DNN structures involve intensive computation and high m...
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07/03/2019
Non-structured DNN Weight Pruning Considered Harmful
Large deep neural network (DNN) models pose the key challenge to energy ...
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05/02/2019
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM
Weight quantization is one of the most important techniques of Deep Neur...
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04/30/2019
ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning
The state-of-art DNN structures involve high computation and great deman...
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03/28/2018
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs
Both industry and academia have extensively investigated hardware accele...
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02/18/2018
Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework
Hardware accelerations of deep learning systems have been extensively in...
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02/03/2018
An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing
With recent trend of wearable devices and Internet of Things (IoTs), it ...
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08/29/2017