Toward accelerating deep learning at scale using specialized hardware in the datacenter

By Kalin Ovtcharov, Olatunji Ruwase, Joo-Young Kim, J. Fowers, Karin Strauss, Eric S. Chung. 2015

48 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Carole-Jean Wu
2. Bilge Acun
3. Chunxing Yin
4. Xing Liu
5. Srilatha Manne
6. Parthasarathy Ranganathan
7. Sarah Bird
8. Shane M. Greenstein

Citations in the corpus

• Socio-Technological Challenges and Opportunities: Paths Forward (2021, background): “…growth opportunities of AI have revolutionized the entire system stack design, resulting in hardware tailored to machine learning execution [Ovtcharov et al., 2015, Chen et al., 2016, Jouppi et al., 2017, Fowers et al., 2018, Shao et al., 2019, Mattson et al., 2020, Henry et al., 2020,…”
• TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models (2021, background): “…interest to design domainspecific accelerators and at-scale infrastructures (Alibaba, 2019; Amazon, 2019; Chung et al., 2018; Fowers et al., 2018; Hazelwood et al., 2018; Jouppi et al., 2017; Lee & Rao, 2019; Mattson et al., 2019; NVIDIA, 2020a; Ovtcharov et al., 2015; Reddi et al., 2019).”