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BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
Mixed-precision quantization can potentially achieve the optimal tradeof...
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On Provable Backdoor Defense in Collaborative Learning
As collaborative learning allows joint training of a model using multipl...
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Hermes: Decentralized Dynamic Spectrum Access System for Massive Devices Deployment in 5G
With the incoming 5G network, the ubiquitous Internet of Things (IoT) de...
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Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Federated learning (FL) is a popular distributed learning framework that...
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GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
Graph-based semi-supervised node classification (GraphSSC) has wide appl...
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Automatic Routability Predictor Development Using Neural Architecture Search
The rise of machine learning technology inspires a boom of its applicati...
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ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition
Scale variance among different sizes of body parts and objects is a chal...
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Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation
Net length is a key proxy metric for optimizing timing and power across ...
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PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
IR drop is a fundamental constraint required by almost all chip designs....
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FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Design flow parameters are of utmost importance to chip design quality a...
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Fast IR Drop Estimation with Machine Learning
IR drop constraint is a fundamental requirement enforced in almost all c...
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Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination
Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the incr...
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Low-Cost Floating-Point Processing in ReRAM for Scientific Computing
We propose ReFloat, a principled approach for low-cost floating-point pr...
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CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems
Neural network-based models augmented with unsupervised pre-trained know...
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Evasion Attacks to Graph Neural Networks via Influence Function
Graph neural networks (GNNs) have achieved state-of-the-art performance ...
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Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs
Link prediction in dynamic graphs (LPDG) is an important research proble...
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LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets
Federated learning is a popular distributed machine learning paradigm wi...
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SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
Training Convolutional Neural Networks (CNNs) usually requires a large n...
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NASGEM: Neural Architecture Search via Graph Embedding Method
Neural Architecture Search (NAS) automates and prospers the design of ne...
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Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces
Deepfake represents a category of face-swapping attacks that leverage ma...
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MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones
Recent research has made great progress in realizing neural style transf...
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TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework with Anonymized Intermediate Representations
The success of deep learning partially benefits from the availability of...
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PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Although state-of-the-art (SOTA) CNNs achieve outstanding performance on...
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TRP: Trained Rank Pruning for Efficient Deep Neural Networks
To enable DNNs on edge devices like mobile phones, low-rank approximatio...
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Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
We consider the blackbox transfer-based targeted adversarial attack thre...
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Transferable Perturbations of Deep Feature Distributions
Almost all current adversarial attacks of CNN classifiers rely on inform...
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Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification
Modern deep neural networks (DNNs) often require high memory consumption...
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Extractive Summarization as Text Matching
This paper creates a paradigm shift with regard to the way we build neur...
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Neural Predictor for Neural Architecture Search
Neural Architecture Search methods are effective but often use complex a...
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AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture
Resource is an important constraint when deploying Deep Neural Networks ...
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Structural sparsification for Far-field Speaker Recognition with GNA
Recently, deep neural networks (DNN) have been widely used in speaker re...
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Traned Rank Pruning for Efficient Deep Neural Networks
To accelerate DNNs inference, low-rank approximation has been widely ado...
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Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30
Generative adversarial network (GAN) has greatly improved the quality of...
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Towards Efficient and Secure Delivery of Data for Deep Learning with Privacy-Preserving
Privacy recently emerges as a severe concern in deep learning, that is, ...
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DeepObfuscator: Adversarial Training Framework for Privacy-Preserving Image Classification
Deep learning has been widely utilized in many computer vision applicati...
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Accelerating CNN Training by Sparsifying Activation Gradients
Gradients to activations get involved in most of the calculations during...
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RED: A ReRAM-based Deconvolution Accelerator
Deconvolution has been widespread in neural networks. For example, it is...
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SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
Designing neural architectures for edge devices is subject to constraint...
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Thread Batching for High-performance Energy-efficient GPU Memory Design
Massive multi-threading in GPU imposes tremendous pressure on memory sub...
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eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA Platform
Simultaneous Localization and Mapping (SLAM) is a critical task for auto...
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Snooping Attacks on Deep Reinforcement Learning
Adversarial attacks have exposed a significant security vulnerability in...
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Low-Power Computer Vision: Status, Challenges, Opportunities
Computer vision has achieved impressive progress in recent years. Meanwh...
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Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual comp...
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SPINBIS: Spintronics based Bayesian Inference System with Stochastic Computing
Bayesian inference is an effective approach for solving statistical lear...
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HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
With the rise of artificial intelligence in recent years, Deep Neural Ne...
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Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack
In recent years, deep neural networks demonstrated state-of-the-art perf...
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Trained Rank Pruning for Efficient Deep Neural Networks
The performance of Deep Neural Networks (DNNs) keeps elevating in recent...
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Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
The success of deep learning research has catapulted deep models into pr...
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LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning
Distributed learning systems have enabled training large-scale models ov...
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A Scalable Pipelined Dataflow Accelerator for Object Region Proposals on FPGA Platform
Region proposal is critical for object detection while it usually poses ...
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