5 citations · 11 across the 6 of their papers we have counts for
6 papers
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…
Measurement-driven neural-network training for integrated magnetic tunnel junction arrays
William A. Borders, Advait Madhavan, Matthew W. Daniels +7
The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeti…
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…
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks
Wen Ma, Pi-Feng Chiu, Won Ho Choi +3
In cloud and edge computing models, it is important that compute devices at the edge be as power efficient as possible. Long short-term memory (LSTM) neural networks have been wide…