30 citations · 36 across the 4 of their papers we have counts for
10 papers
Restructurable Activation Networks
Kartikeya Bhardwaj, James Ward, Caleb Tung +6
Is it possible to restructure the non-linear activation functions in a deep network to create hardware-efficient models? To address this question, we propose a new paradigm called…
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…
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning
Wei Chen, Kartikeya Bhardwaj, Radu Marculescu
In this paper, we identify a new phenomenon called activation-divergence which occurs in Federated Learning (FL) due to data heterogeneity (i.e., data being non-IID) across multipl…
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…
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,…