5 citations · 5 across the 4 of their papers we have counts for
4 papers · 1 filter
RACE-AIMC: Selective Inference for Heterogeneous Analog In-Memory Accelerators at the Edge
Osama Yousuf, Martin Lueker-Boden
Analog in-memory computing (AIMC) speeds up neural-network inference by doing the arithmetic directly inside a memory array, instead of shuttling weights back and forth between mem…
XBTorch: A Unified Framework for Modeling and Co-Design of Crossbar-Based Deep Learning Accelerators
Osama Yousuf, Andreu L. Glasmann, Martin Lueker-Boden +2
Emerging memory technologies have gained significant attention as a promising pathway to overcome the limitations of conventional computing architectures in deep learning applicati…
Layer Ensemble Averaging for Improving Memristor-Based Artificial Neural Network Performance
Osama Yousuf, Brian Hoskins, Karthick Ramu +8
Artificial neural networks have advanced due to scaling dimensions, but conventional computing faces inefficiency due to the von Neumann bottleneck. In-memory computation architect…
Device Modeling Bias in ReRAM-based Neural Network Simulations
Osama Yousuf, Imtiaz Hossen, Matthew W. Daniels +3
Data-driven modeling approaches such as jump tables are promising techniques to model populations of resistive random-access memory (ReRAM) or other emerging memory devices for har…