3 citations · 6 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
Inclusive-PIM: Hardware-Software Co-design for Broad Acceleration on Commercial PIM Architectures
Johnathan Alsop, Shaizeen Aga, Mohamed Ibrahim +3
Continual demand for memory bandwidth has made it worthwhile for memory vendors to reassess processing in memory (PIM), which enables higher bandwidth by placing compute units in/n…
Collaborative Acceleration for FFT on Commercial Processing-In-Memory Architectures
Mohamed Assem Ibrahim, Shaizeen Aga
This paper evaluates the efficacy of recent commercial processing-in-memory (PIM) solutions to accelerate fast Fourier transform (FFT), an important primitive across several domain…
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…
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…