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05/30/2023
Dynamic Sparsity Is Channel-Level Sparsity Learner
Sparse training has received an upsurging interest in machine learning d...
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05/03/2023
Rethinking Graph Lottery Tickets: Graph Sparsity Matters
Lottery Ticket Hypothesis (LTH) claims the existence of a winning ticket...
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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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09/03/2022
Sion: Elastic Serverless Cloud Storage
Cloud object storage such as AWS S3 is cost-effective and highly elastic...
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02/09/2022
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets
The lottery ticket hypothesis (LTH) has shown that dense models contain ...
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12/20/2021
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks
Deep neural networks (DNNs) have been proven to be effective in solving ...
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07/09/2021
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
As both machine learning models and the datasets on which they are evalu...
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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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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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05/11/2020
CSB-RNN: A Faster-than-Realtime RNN Acceleration Framework with Compressed Structured Blocks
Recurrent neural networks (RNNs) have been widely adopted in temporal se...
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04/12/2020
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
To address the large model size and intensive computation requirement of...
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03/13/2020
A Privacy-Preserving DNN Pruning and Mobile Acceleration Framework
To facilitate the deployment of deep neural networks (DNNs) on resource-...
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02/11/2020
PCNN: Pattern-based Fine-Grained Regular Pruning towards Optimizing CNN Accelerators
Weight pruning is a powerful technique to realize model compression. We ...
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01/28/2020
InfiniCache: Exploiting Ephemeral Serverless Functions to Build a Cost-Effective Memory Cache
Internet-scale web applications are becoming increasingly storage-intens...
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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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01/23/2020
BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
Accelerating DNN execution on various resource-limited computing platfor...
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01/20/2020
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
Weight pruning has been widely acknowledged as a straightforward and eff...
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01/01/2020
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
With the emergence of a spectrum of high-end mobile devices, many applic...
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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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09/06/2019
PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices
Model compression techniques on Deep Neural Network (DNN) have been wide...
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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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07/06/2019
AutoSlim: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates
Structured weight pruning is a representative model compression techniqu...
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07/06/2019
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates
Structured weight pruning is a representative model compression techniqu...
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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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05/02/2019
26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone
With the rapid emergence of a spectrum of high-end mobile devices, many ...
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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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03/23/2019
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Weight pruning and weight quantization are two important categories of D...
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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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03/20/2018
C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing
Increasing malicious users have sought practices to leverage 3D printing...
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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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02/15/2018
Image Dataset for Visual Objects Classification in 3D Printing
The rapid development in additive manufacturing (AM), also known as 3D p...
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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
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices
Large-scale deep neural networks (DNNs) are both compute and memory inte...
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11/25/2016
