Search Results for author:
Found 19 papers, 6 papers with code
Loss Attitude Aware Energy Management for Signal Detection
The amount of resource consumption that maximizes the humans' subjective utility is derived to characterize the actual behavior of humans.
Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting
Deep neural networks (DNNs) have shown to provide superb performance in many real life applications, but their large computation cost and storage requirement have prevented them from being deployed to many edge and internet-of-things (IoT) devices.
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks
Our experiments show that GS patterns consistently make better trade-offs between accuracy and computation efficiency compared to conventional structured sparse patterns.
Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search
Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices.
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks.
Computation on Sparse Neural Networks: an Inspiration for Future Hardware
We show that for practically complicated problems, it is more beneficial to search large and sparse models in the weight dominated region.
A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i. e., static regularization-based pruning and dynamic regularization-based pruning.
BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem.
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms.
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM
Memristor-based weight pruning and weight quantization have been seperately investigated and proven effectiveness in reducing area and power consumption compared to the original DNN model.
Adversarial Attack Generation Empowered by Min-Max Optimization
In this paper, we show how a general framework of min-max optimization over multiple domains can be leveraged to advance the design of different types of adversarial attacks.
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
A recent work developed a systematic frame-work of DNN weight pruning using the advanced optimization technique ADMM (Alternating Direction Methods of Multipliers), achieving one of state-of-art in weight pruning results.
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers
The first part of ADMM-NN is a systematic, joint framework of DNN weight pruning and quantization using ADMM.
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM
Both DNN weight pruning and clustering/quantization, as well as their combinations, can be solved in a unified manner.
Progressive Weight Pruning of Deep Neural Networks using ADMM
Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates.
StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs
Without loss of accuracy on the AlexNet model, we achieve 2. 58X and 3. 65X average measured speedup on two GPUs, clearly outperforming the prior work.
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
We first formulate the weight pruning problem of DNNs as a nonconvex optimization problem with combinatorial constraints specifying the sparsity requirements, and then adopt the ADMM framework for systematic weight pruning.
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM).
