85 citations · 166 across the 5 of their papers we have counts for
7 papers
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…
Dream Distillation: A Data-Independent Model Compression Framework
Kartikeya Bhardwaj, Naveen Suda, Radu Marculescu
Model compression is eminently suited for deploying deep learning on IoT-devices. However, existing model compression techniques rely on access to the original or some alternate da…
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…
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…
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…