20 citations · 31 across the 6 of their papers we have counts for
6 papers
Efficient Pruning for Machine Learning Under Homomorphic Encryption
Ehud Aharoni, Moran Baruch, Pradip Bose +8
Privacy-preserving machine learning (PPML) solutions are gaining widespread popularity. Among these, many rely on homomorphic encryption (HE) that offers confidentiality of the mod…
HetSched: Quality-of-Mission Aware Scheduling for Autonomous Vehicle SoCs
Aporva Amarnath, Subhankar Pal, Hiwot Kassa +6
Systems-on-Chips (SoCs) that power autonomous vehicles (AVs) must meet stringent performance and safety requirements prior to deployment. With increasing complexity in AV applicati…
STOMP: A Tool for Evaluation of Scheduling Policies in Heterogeneous Multi-Processors
Augusto Vega, Aporva Amarnath, John-David Wellman +6
The proliferation of heterogeneous chip multiprocessors in recent years has reached unprecedented levels. Traditional homogeneous platforms have shown fundamental limitations when…
Improving Efficiency in Large-Scale Decentralized Distributed Training
Wei Zhang, Xiaodong Cui, Abdullah Kayi +9
Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to p…
Touché: Towards Ideal and Efficient Cache Compression By Mitigating Tag Area Overheads
Seokin Hong, Bulent Abali, Alper Buyuktosunoglu +2
Compression is seen as a simple technique to increase the effective cache capacity. Unfortunately, compression techniques either incur tag area overheads or restrict data placement…
A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition
Wei Zhang, Xiaodong Cui, Ulrich Finkler +6
Modern Automatic Speech Recognition (ASR) systems rely on distributed deep learning to for quick training completion. To enable efficient distributed training, it is imperative tha…