79 citations · 355 across the 59 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…
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…
Work-in-Progress: A Simulation Framework for Domain-Specific System-on-Chips
Samet E. Arda, Anish NK, A. Alper Goksoy +6
Heterogeneous system-on-chips (SoCs) have become the standard embedded computing platforms due to their potential to deliver superior performance and energy efficiency compared to…
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…
On Network Science and Mutual Information for Explaining Deep Neural Networks
Brian Davis, Umang Bhatt, Kartikeya Bhardwaj +2
In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforwar…