6 citations · 7 across the 2 of their papers we have counts for
3 papers
cs.AR2024★ 1 cited
OpenGeMM: A High-Utilization GeMM Accelerator Generator with Lightweight RISC-V Control and Tight Memory Coupling
Xiaoling Yi, Ryan Antonio, Joren Dumoulin +4
Deep neural networks (DNNs) face significant challenges when deployed on resource-constrained extreme edge devices due to their computational and data-intensive nature. While stand…
eess.SP2024
Analog or Digital In-memory Computing? Benchmarking through Quantitative Modeling
Jiacong Sun, Pouya Houshmand, Marian Verhelst
In-Memory Computing (IMC) has emerged as a promising paradigm for energy-efficient, throughput-efficient and area-efficient machine learning at the edge. However, the differences i…
cs.AR2023★ 6 cited
Benchmarking and modeling of analog and digital SRAM in-memory computing architectures
Pouya Houshmand, Jiacong Sun, Marian Verhelst
In-memory-computing is emerging as an efficient hardware paradigm for deep neural network accelerators at the edge, enabling to break the memory wall and exploit massive computatio…