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cs.AR2024
Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni +2
In this paper, we present a novel technique to search for hardware architectures of accelerators optimized for end-to-end training of deep neural networks (DNNs). Our approach addr…
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…