14 citations · 36 across the 26 of their papers we have counts for
9 papers · 1 filter
MobileTL: On-device Transfer Learning with Inverted Residual Blocks
Hung-Yueh Chiang, Natalia Frumkin, Feng Liang +1
Transfer learning on edge is challenging due to on-device limited resources. Existing work addresses this issue by training a subset of parameters or adding model patches. Develope…
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers
Natalia Frumkin, Dibakar Gope, Diana Marculescu
When considering post-training quantization, prior work has typically focused on developing a mixed precision scheme or learning the best way to partition a network for quantizatio…
AVE-CLIP: AudioCLIP-based Multi-window Temporal Transformer for Audio Visual Event Localization
Tanvir Mahmud, Diana Marculescu
An audio-visual event (AVE) is denoted by the correspondence of the visual and auditory signals in a video segment. Precise localization of the AVEs is very challenging since it de…
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
Feng Liang, Bichen Wu, Xiaoliang Dai +6
Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage…
Play It Cool: Dynamic Shifting Prevents Thermal Throttling
Yang Zhou, Feng Liang, Ting-wu Chin +1
Machine learning (ML) has entered the mobile era where an enormous number of ML models are deployed on edge devices. However, running common ML models on edge devices continuously…
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +4
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied precision or quantization l…
Efficient Deep Learning Using Non-Volatile Memory Technology
Ahmet Inci, Mehmet Meric Isgenc, Diana Marculescu
Embedded machine learning (ML) systems have now become the dominant platform for deploying ML serving tasks and are projected to become of equal importance for training ML models.…
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +3
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantizati…
SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners
Feng Liang, Yangguang Li, Diana Marculescu
Recently, self-supervised Masked Autoencoders (MAE) have attracted unprecedented attention for their impressive representation learning ability. However, the pretext task, Masked I…