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04/03/2023
RPTQ: Reorder-based Post-training Quantization for Large Language Models
Large-scale language models (LLMs) have demonstrated outstanding perform...
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03/23/2023
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Post-training quantization (PTQ) is a popular method for compressing dee...
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10/12/2022
Latency-aware Spatial-wise Dynamic Networks
Spatial-wise dynamic convolution has become a promising approach to impr...
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05/20/2022
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business ...
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12/12/2021
Anomaly Crossing: A New Method for Video Anomaly Detection as Cross-domain Few-shot Learning
Video anomaly detection aims to identify abnormal events that occurred i...
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11/24/2021
PTQ4ViT: Post-Training Quantization Framework for Vision Transformers
Quantization is one of the most effective methods to compress neural net...
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11/01/2021
GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing
Recently, Graph Convolutional Networks (GCNs) have become state-of-the-a...
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10/18/2021
Energon: Towards Efficient Acceleration of Transformers Using Dynamic Sparse Attention
In recent years, transformer models have revolutionized Natural Language...
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10/15/2021
PTQ-SL: Exploring the Sub-layerwise Post-training Quantization
Network quantization is a powerful technique to compress convolutional n...
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08/26/2021
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
Sampling is a critical operation in the training of Graph Neural Network...
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08/24/2021
METRO: A Software-Hardware Co-Design of Interconnections for Spatial DNN Accelerators
Tiled spatial architectures have proved to be an effective solution to b...
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05/11/2021
Agatha: Smart Contract for DNN Computation
Smart contract is one of the core features of Ethereum and has inspired ...
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11/12/2020
Customizing Trusted AI Accelerators for Efficient Privacy-Preserving Machine Learning
The use of trusted hardware has become a promising solution to enable pr...
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09/19/2020
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference
Dynamic inference is a feasible way to reduce the computational cost of ...
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11/16/2019
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
Recently, dynamic inference has emerged as a promising way to reduce the...
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10/05/2019
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Bayesian deep learning is recently regarded as an intrinsic way to chara...
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08/21/2019
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
In this paper, we aim to understand the generalization properties of gen...
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07/26/2019
Detection of Malfunctioning Smart Electricity Meter
In this paper, a method for malfunctioning smart meter detection, based ...
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06/03/2019
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Recently, deep learning as a service (DLaaS) has emerged as a promising ...
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05/30/2019
P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification
Recently, deep convolutional neural networks (CNNs) have achieved great ...
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06/30/2018
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Pathological glomerulus classification plays a key role in the diagnosis...
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01/07/2018
EdgeFlow: Open-Source Multi-layer Data Flow Processing in Edge Computing for 5G and Beyond
Edge computing has evolved to be a promising avenue to enhance the syste...
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12/16/2017
