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07/05/2023
Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities
Recently, zero-shot (or training-free) Neural Architecture Search (NAS) ...
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07/01/2023
MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications
Traditionally, convolutional neural networks (CNN) and vision transforme...
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05/13/2023
TIPS: Topologically Important Path Sampling for Anytime Neural Networks
Anytime neural networks (AnytimeNNs) are a promising solution to adaptiv...
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03/29/2023
Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation
Transformers have shown great success in medical image segmentation. How...
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01/26/2023
ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients
Neural Architecture Search (NAS) is widely used to automatically design ...
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01/01/2023
Efficient On-device Training via Gradient Filtering
Despite its importance for federated learning, continuous learning and m...
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03/31/2022
Dynamic Multimodal Fusion
Deep multimodal learning has achieved great progress in recent years. Ho...
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01/31/2022
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning
Graph Neural Networks (GNNs) have demonstrated a great potential in a va...
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09/22/2021
DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs
Domain-specific systems-on-chip (DSSoCs) aim at bridging the gap between...
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08/01/2021
FLASH: Fast Neural Architecture Search with Hardware Optimization
Neural architecture search (NAS) is a promising technique to design effi...
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08/25/2020
New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design
In this paper, we first highlight three major challenges to large-scale ...
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07/18/2020
Runtime Task Scheduling using Imitation Learning for Heterogeneous Many-Core Systems
Domain-specific systems-on-chip, a class of heterogeneous many-core syst...
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04/08/2020
Centralized and decentralized isolation strategies and their impact on the COVID-19 pandemic dynamics
The infectious diseases are spreading due to human interactions enabled ...
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04/07/2020
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning
In this paper, we identify a new phenomenon called activation-divergence...
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03/19/2020
DS3: A System-Level Domain-Specific System-on-Chip Simulation Framework
Heterogeneous systems-on-chip (SoCs) are highly favorable computing plat...
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10/23/2019
EdgeAI: A Vision for Deep Learning in IoT Era
The significant computational requirements of deep learning present a ma...
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10/02/2019
Towards Unifying Neural Architecture Space Exploration and Generalization
In this paper, we address a fundamental research question of significant...
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08/10/2019
Work-in-Progress: A Simulation Framework for Domain-Specific System-on-Chips
Heterogeneous system-on-chips (SoCs) have become the standard embedded c...
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07/26/2019
Memory- and Communication-Aware Model Compression for Distributed Deep Learning Inference on IoT
Model compression has emerged as an important area of research for deplo...
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05/17/2019
Dream Distillation: A Data-Independent Model Compression Framework
Model compression is eminently suited for deploying deep learning on IoT...
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01/20/2019
NIF: A Framework for Quantifying Neural Information Flow in Deep Networks
In this paper, we present a new approach to interpreting deep learning m...
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12/06/2018
Climate Anomalies vs Air Pollution: Carbon Emissions and Anomaly Networks
This project aims to shed light on how man-made carbon emissions are aff...
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12/01/2018
A Dynamic Network and Representation LearningApproach for Quantifying Economic Growth fromSatellite Imagery
Quantifying the improvement in human living standard, as well as the cit...
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10/20/2018
Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems
The rising use of deep learning and other big-data algorithms has led to...
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12/05/2017
On-Chip Communication Network for Efficient Training of Deep Convolutional Networks on Heterogeneous Manycore Systems
Convolutional Neural Networks (CNNs) have shown a great deal of success ...
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11/30/2017
