Microscaling Data Formats for Deep Learning

1 code implementation • 16 Oct 2023 • Bita Darvish Rouhani, Ritchie Zhao, Ankit More, Mathew Hall, Alireza Khodamoradi, Summer Deng, Dhruv Choudhary, Marius Cornea, Eric Dellinger, Kristof Denolf, Stosic Dusan, Venmugil Elango, Maximilian Golub, Alexander Heinecke, Phil James-Roxby, Dharmesh Jani, Gaurav Kolhe, Martin Langhammer, Ada Li, Levi Melnick, Maral Mesmakhosroshahi, Andres Rodriguez, Michael Schulte, Rasoul Shafipour, Lei Shao, Michael Siu, Pradeep Dubey, Paulius Micikevicius, Maxim Naumov, Colin Verrilli, Ralph Wittig, Doug Burger, Eric Chung

Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications.

Friction

With Shared Microexponents, A Little Shifting Goes a Long Way

no code implementations • 16 Feb 2023 • Bita Rouhani, Ritchie Zhao, Venmugil Elango, Rasoul Shafipour, Mathew Hall, Maral Mesmakhosroshahi, Ankit More, Levi Melnick, Maximilian Golub, Girish Varatkar, Lei Shao, Gaurav Kolhe, Dimitry Melts, Jasmine Klar, Renee L'Heureux, Matt Perry, Doug Burger, Eric Chung, Zhaoxia Deng, Sam Naghshineh, Jongsoo Park, Maxim Naumov

This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning.

Quantization Recommendation Systems

Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs

no code implementations • 14 Dec 2020 • Andrew Boutros, Mathew Hall, Nicolas Papernot, Vaughn Betz

We find that even when using the strongest attacker circuit, the prediction accuracy of the DL accelerator is not compromised when running at its safe operating frequency.