research
∙
08/28/2023
Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks
Recommendation models are vital in delivering personalized user experien...
share
research
∙
07/05/2023
FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
Federated Learning (FL) allows machine learning models to train locally ...
research
∙
12/23/2022
Scalable and Secure Row-Swap: Efficient and Safe Row Hammer Mitigation in Memory Systems
As Dynamic Random Access Memories (DRAM) scale, they are becoming increa...
research
∙
07/08/2022
The Dirty Secret of SSDs: Embodied Carbon
Scalable Solid-State Drives (SSDs) have revolutionized the way we store ...
research
∙
04/11/2022
Heterogeneous Acceleration Pipeline for Recommendation System Training
Recommendation systems are unique as they show a conflation of compute a...
research
∙
03/01/2021
High-Performance Training by Exploiting Hot-Embeddings in Recommendation Systems
Recommendation models are commonly used learning models that suggest rel...
research
∙
09/02/2019
Touché: Towards Ideal and Efficient Cache Compression By Mitigating Tag Area Overheads
Compression is seen as a simple technique to increase the effective cach...
research
∙
05/08/2018
LISA: Increasing Internal Connectivity in DRAM for Fast Data Movement and Low Latency
This paper summarizes the idea of Low-Cost Interlinked Subarrays (LISA),...
research
∙
04/13/2017
