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20172023
most citedAccelerating Recommendation System Training by Leveraging Popular Choices

53 citations · 180 across the 10 of their papers we have counts for

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cs.AR2022★ 51 cited

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…

cs.AR2022★ 2 cited

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…

cs.AR2019★ 20 cited

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…

cs.AR2018

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…

cs.AR2017★ 2 cited

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…