79 citations · 187 across the 8 of their papers we have counts for
19 papers
DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs
A. Alper Goksoy, Anish Krishnakumar, Md Sahil Hassan +4
Domain-specific systems-on-chip (DSSoCs) aim at bridging the gap between application-specific integrated circuits (ASICs) and general-purpose processors. Traditional operating syst…
FLASH: Fast Neural Architecture Search with Hardware Optimization
Guihong Li, Sumit K. Mandal, Umit Y. Ogras +1
Neural architecture search (NAS) is a promising technique to design efficient and high-performance deep neural networks (DNNs). As the performance requirements of ML applications g…
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…
Runtime Task Scheduling using Imitation Learning for Heterogeneous Many-Core Systems
Anish Krishnakumar, Samet E. Arda, A. Alper Goksoy +4
Domain-specific systems-on-chip, a class of heterogeneous many-core systems, are recognized as a key approach to narrow down the performance and energy-efficiency gap between custo…
Centralized and decentralized isolation strategies and their impact on the COVID-19 pandemic dynamics
Alexandru Topirceanu, Mihai Udrescu, Radu Marculescu
The infectious diseases are spreading due to human interactions enabled by various social networks. Therefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-ph…
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…