35 citations · 67 across the 4 of their papers we have counts for
4 papers
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators
Hadi Esmaeilzadeh, Soroush Ghodrati, Andrew B. Kahng +8
Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design…
Scalable Smartphone Cluster for Deep Learning
Byunggook Na, Jaehee Jang, Seongsik Park +7
Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single s…
In-RDBMS Hardware Acceleration of Advanced Analytics
Divya Mahajan, Joon Kyung Kim, Jacob Sacks +3
The data revolution is fueled by advances in machine learning, databases, and hardware design. Programmable accelerators are making their way into each of these areas independently…
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks
Hardik Sharma, Jongse Park, Naveen Suda +5
Fully realizing the potential of acceleration for Deep Neural Networks (DNNs) requires understanding and leveraging algorithmic properties. This paper builds upon the algorithmic i…