From the 1 of 4 linked papers with an AI index.
4 papers
Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer
Rebecca Pelke, José Cubero-Cascante, Nils Bosbach +5
The paper presents CIM-Explorer, a modular toolkit that compiles, maps, and simulates binary and ternary neural network inference on RRAM crossbars, enabling design‑space explorati…
Optimizing ML Workload Partitioning between CPUs and CIM Accelerators for Heterogeneous Computing
Joel Klein, Rebecca Pelke, Roberto Laudani +2
Computing-in-Memory (CIM) accelerators execute Matrix-Vector Multiplications (MVMs) in memory, making them a compelling solution for Machine Learning (ML) workloads. However, exist…
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…
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…