Google Workloads for Consumer Devices-Reference-Cited by-同舟云学术

Google Workloads for Consumer Devices

Published:2018-03-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems
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Author:

Boroumand Amirali¹,Ghose Saugata²,Kim Youngsok³,Ausavarungnirun Rachata²,Shiu Eric⁴,Thakur Rahul⁴,Kim Daehyun⁵,Kuusela Aki⁴,Knies Allan⁴,Ranganathan Parthasarathy⁴,Mutlu Onur⁶

Affiliation:

1. Carnegie Mellon University, Moffett Field, CA, USA

2. Carnegie Mellon University, Pittsburgh, PA, USA

3. Seoul National University, Seoul, South Korea

4. Google, Mountain View, USA

5. Samsung Research, Google, Seoul, South Korea

6. ETH Zürich&Carnegie Mellon University, Zurich, Switzerland

Funder

Google Huawei Intel VMware Semiconductor Research Corporation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3173162.3173177

Reference164 articles.

1. Fathom: reference workloads for modern deep learning methods

2. A scalable processing-in-memory accelerator for parallel graph processing

3. PIM-enabled instructions

4. Data reorganization in memory using 3D-stacked DRAM

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