1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.AR2024
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…
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★ 1 cited
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…