3 papers
cs.AR2023
Just-in-time Quantization with Processing-In-Memory for Efficient ML Training
Mohamed Assem Ibrahim, Shaizeen Aga, Ada Li +2
Data format innovations have been critical for machine learning (ML) scaling, which in turn fuels ground-breaking ML capabilities. However, even in the presence of low-precision fo…
cs.AR2023
Egalitarian ORAM: Wear-Leveling for ORAM
Yi Zheng, Aasheesh Kolli, Shaizeen Aga
While non-volatile memories (NVMs) provide several desirable characteristics like better density and comparable energy efficiency than DRAM, DRAM-like performance, and disk-like du…
cs.AR2023
Computation vs. Communication Scaling for Future Transformers on Future Hardware
Suchita Pati, Shaizeen Aga, Mahzabeen Islam +2
Scaling neural network models has delivered dramatic quality gains across ML problems. However, this scaling has increased the reliance on efficient distributed training techniques…