8 citations · 25 across the 10 of their papers we have counts for
7 papers · 1 filter
Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems
Shuo Huai, Di Liu, Hao Kong +5
Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy. Howe…
Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization
Shuo Huai, Di Liu, Hao Kong +4
Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during th…
Compressing CNN models for resource-constrained systems by channel and layer pruning
Ahmed Sadaqa, Di Liu
Convolutional Neural Networks (CNNs) have achieved significant breakthroughs in various fields. However, these advancements have led to a substantial increase in the complexity and…
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
Xiangzhong Luo, Di Liu, Hao Kong +4
Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to…
You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms
Xiangzhong Luo, Di Liu, Hao Kong +3
Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural…
HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search
Xiangzhong Luo, Di Liu, Shuo Huai +1
In this paper, we present a novel multi-objective hardware-aware neural architecture search (NAS) framework, namely HSCoNAS, to automate the design of deep neural networks (DNNs) w…