30 citations · 36 across the 6 of their papers we have counts for
4 papers · 1 filter
New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design
Kartikeya Bhardwaj, Wei Chen, Radu Marculescu
In this paper, we first highlight three major challenges to large-scale adoption of deep learning at the edge: (i) Hardware-constrained IoT devices, (ii) Data security and privacy…
How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?
Kartikeya Bhardwaj, Guihong Li, Radu Marculescu
DenseNets introduce concatenation-type skip connections that achieve state-of-the-art accuracy in several computer vision tasks. In this paper, we reveal that the topology of the c…
Memory- and Communication-Aware Model Compression for Distributed Deep Learning Inference on IoT
Kartikeya Bhardwaj, Chingyi Lin, Anderson Sartor +1
Model compression has emerged as an important area of research for deploying deep learning models on Internet-of-Things (IoT). However, for extremely memory-constrained scenarios,…
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…