2 papers
cs.LG2026
Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Accelerators
Rebecca Pelke, Joel Klein, Jose Cubero-Cascante +3
Computing-in-Memory (CIM) accelerators are a promising solution for accelerating Machine Learning (ML) workloads, as they perform Matrix-Vector Multiplications (MVMs) on crossbar a…
eess.SP2025
Evaluating the Scalability of Binary and Ternary CNN Workloads on RRAM-based Compute-in-Memory Accelerators
José Cubero-Cascante, Rebecca Pelke, Noah Flohr +3
The increasing computational demand of Convolutional Neural Networks (CNNs) necessitates energy-efficient acceleration strategies. Compute-in-Memory (CIM) architectures based on Re…