98 citations · 270 across the 20 of their papers we have counts for
3 papers · 1 filter
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
Ahmed T. Elthakeb, Prannoy Pilligundla, FatemehSadat Mireshghallah +2
Deep Neural Networks (DNNs) typically require massive amount of computation resource in inference tasks for computer vision applications. Quantization can significantly reduce DNN…
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks
Amir Yazdanbakhsh, Hajar Falahati, Philip J. Wolfe +3
Generative Adversarial Networks (GANs) are one of the most recent deep learning models that generate synthetic data from limited genuine datasets. GANs are on the frontier as furth…
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…