1 citations · 1 across the 6 of their papers we have counts for
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FinGraV: Methodology for Fine-Grain GPU Power Visibility and Insights
Varsha Singhania, Shaizeen Aga, Mohamed Assem Ibrahim
Ubiquity of AI makes optimizing GPU power a priority as large GPU-based clusters are often employed to train and serve AI models. An important first step in optimizing GPU power co…
Balanced Data Placement for GEMV Acceleration with Processing-In-Memory
Mohamed Assem Ibrahim, Mahzabeen Islam, Shaizeen Aga
With unprecedented demand for generative AI (GenAI) inference, acceleration of primitives that dominate GenAI such as general matrix-vector multiplication (GEMV) is receiving consi…
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…