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Taesik Na

2 accepted papers

2020

Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point

NeurIPS 2020poster

In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of datatypes developed for production cloud-scale inferencing on custom hardware. Through the co-evolution of hardware design and algorithms, MSFP achieves accuracy comparable to or better than industry standards Bf…

2018

Cascade Adversarial Machine Learning Regularized with a Unified Embedding

ICLR 2018poster

Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks. To address this challenge, we first show iteratively generated adversarial images easily transfer between networks trained with the s…