53 citations · 180 across the 10 of their papers we have counts for
10 papers
Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks
Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan +1
Recommendation models are vital in delivering personalized user experiences by leveraging the correlation between multiple input features. However, deep learning-based recommendati…
FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
Irene Wang, Prashant J. Nair, Divya Mahajan
Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server. This approach safeguards user…
Scalable and Secure Row-Swap: Efficient and Safe Row Hammer Mitigation in Memory Systems
Jeonghyun Woo, Gururaj Saileshwar, Prashant J. Nair
As Dynamic Random Access Memories (DRAM) scale, they are becoming increasingly susceptible to Row Hammer. By rapidly activating rows of DRAM cells (aggressor rows), attackers can e…
The Dirty Secret of SSDs: Embodied Carbon
Swamit Tannu, Prashant J. Nair
Scalable Solid-State Drives (SSDs) have ushered in a transformative era in data storage and accessibility, spanning both data centers and portable devices. However, the strides mad…
Heterogeneous Acceleration Pipeline for Recommendation System Training
Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan +1
Recommendation models rely on deep learning networks and large embedding tables, resulting in computationally and memory-intensive processes. These models are typically trained usi…
Accelerating Simulation of Quantum Circuits under Noise via Computational Reuse
Meng Wang, Swamit Tannu, Prashant J. Nair
To realize the full potential of quantum computers, we must mitigate qubit errors by developing noise-aware algorithms, compilers, and architectures. Thus, simulating quantum progr…
Accelerating Recommendation System Training by Leveraging Popular Choices
Muhammad Adnan, Yassaman Ebrahimzadeh Maboud, Divya Mahajan +1
Recommender models are commonly used to suggest relevant items to a user for e-commerce and online advertisement-based applications. These models use massive embedding tables to st…
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…
LISA: Increasing Internal Connectivity in DRAM for Fast Data Movement and Low Latency
Kevin K. Chang, Prashant J. Nair, Saugata Ghose +3
This paper summarizes the idea of Low-Cost Interlinked Subarrays (LISA), which was published in HPCA 2016, and examines the work's significance and future potential. Contemporary s…
Architectural Techniques to Enable Reliable and Scalable Memory Systems
Prashant J. Nair
High capacity and scalable memory systems play a vital role in enabling our desktops, smartphones, and pervasive technologies like Internet of Things (IoT). Unfortunately, memory s…