1.9k citations · 2.4k across the 10 of their papers we have counts for
6 papers · 1 filter
EdgeAI: A Vision for Deep Learning in IoT Era
Kartikeya Bhardwaj, Naveen Suda, Radu Marculescu
The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices. Here, we envision a new par…
Rethinking Machine Learning Development and Deployment for Edge Devices
Liangzhen Lai, Naveen Suda
Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks. As…
Federated Learning with Non-IID Data
Yue Zhao, Meng Li, Liangzhen Lai +3
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data…
Not All Ops Are Created Equal!
Liangzhen Lai, Naveen Suda, Vikas Chandra
Efficient and compact neural network models are essential for enabling the deployment on mobile and embedded devices. In this work, we point out that typical design metrics for gau…
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training
Meng Li, Liangzhen Lai, Naveen Suda +2
Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based traini…
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
Liangzhen Lai, Naveen Suda, Vikas Chandra
Deep convolutional neural network (CNN) inference requires significant amount of memory and computation, which limits its deployment on embedded devices. To alleviate these problem…