1.9k citations · 2.4k across the 10 of their papers we have counts for
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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…
CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs
Liangzhen Lai, Naveen Suda, Vikas Chandra
Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices performing data analytics right at the source, reducing latency as well as energy consumption f…