61 citations · 97 across the 5 of their papers we have counts for
5 papers
Robustifying Adversarial Training to the Union of Perturbation Models
Ameya D. Patil, Michael Tuttle, Alexander G. Schwing +1
Classical adversarial training (AT) frameworks are designed to achieve high adversarial accuracy against a single attack type, typically norm-bounded perturbations. R…
Compressing GANs using Knowledge Distillation
Angeline Aguinaldo, Ping-Yeh Chiang, Alex Gain +3
Generative Adversarial Networks (GANs) have been used in several machine learning tasks such as domain transfer, super resolution, and synthetic data generation. State-of-the-art G…
Shannon-inspired Statistical Computing to Enable Spintronics
Ameya D. Patil, Sasikanth Manipatruni, Dmitri Nikonov +2
Modern computing systems based on the von Neumann architecture are built from silicon complementary metal oxide semiconductor (CMOS) transistors that need to operate under practica…
A 481pJ/decision 3.4M decision/s Multifunctional Deep In-memory Inference Processor using Standard 6T SRAM Array
Mingu Kang, Sujan Gonugondla, Ameya Patil +1
This paper describes a multi-functional deep in-memory processor for inference applications. Deep in-memory processing is achieved by embedding pitch-matched low-SNR analog process…
Understanding the Energy and Precision Requirements for Online Learning
Charbel Sakr, Ameya Patil, Sai Zhang +2
It is well-known that the precision of data, hyperparameters, and internal representations employed in learning systems directly impacts its energy, throughput, and latency. The pr…