Trustworthy AI Inference Systems: An Industry Research View

no code implementations • 10 Aug 2020 • Rosario Cammarota, Matthias Schunter, Anand Rajan, Fabian Boemer, Ágnes Kiss, Amos Treiber, Christian Weinert, Thomas Schneider, Emmanuel Stapf, Ahmad-Reza Sadeghi, Daniel Demmler, Joshua Stock, Huili Chen, Siam Umar Hussain, Sadegh Riazi, Farinaz Koushanfar, Saransh Gupta, Tajan Simunic Rosing, Kamalika Chaudhuri, Hamid Nejatollahi, Nikil Dutt, Mohsen Imani, Kim Laine, Anuj Dubey, Aydin Aysu, Fateme Sadat Hosseini, Chengmo Yang, Eric Wallace, Pamela Norton

Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time.

RAPIDNN: In-Memory Deep Neural Network Acceleration Framework

no code implementations • 15 Jun 2018 • Mohsen Imani, Mohammad Samragh, Yeseong Kim, Saransh Gupta, Farinaz Koushanfar, Tajana Rosing

To enable in-memory processing, RAPIDNN reinterprets a DNN model and maps it into a specialized accelerator, which is designed using non-volatile memory blocks that model four fundamental DNN operations, i. e., multiplication, addition, activation functions, and pooling.

Clustering speech-recognition +3