activity
20242026
most citedNQC2: A Non-Intrusive QEMU Code Coverage Plugin

2 citations · 3 across the 13 of their papers we have counts for

collaborators

13 papers

cs.ET2026

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…

cs.SE2026

Stateful Embedded Fuzzing with Peripheral-Accurate SystemC Virtual Prototypes

Chiara Ghinami, Igor Pontes Tresolavy, Luis Seibt +2

The increasing complexity of embedded software has made comprehensive manual testing impractical, motivating the use of automated techniques such as fuzzing. Coverage-guided fuzzer…

cs.SE2026★ 2 cited

NQC2: A Non-Intrusive QEMU Code Coverage Plugin

Nils Bosbach, Alwalid Salama, Lukas Jünger +4

Code coverage analysis has become a standard approach in software development, facilitating the assessment of test suite effectiveness, the identification of under-tested code segm…

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…

cs.SE2025

Leveraging SystemC-TLM-based Virtual Prototypes for Embedded Software Fuzzing

Chiara Ghinami, Jonas Winzer, Nils Bosbach +4

SystemC-based virtual prototypes have emerged as widely adopted tools to test software ahead of hardware availability, reducing the time-to-market and improving software reliabilit…

cs.SE2025

FMI Meets SystemC: A Framework for Cross-Tool Virtual Prototyping

Nils Bosbach, Meik Schmidt, Lukas Jünger +2

As systems become more complex, the demand for thorough testing and virtual prototyping grows. To simulate whole systems, multiple tools are usually needed to cover different parts…

cs.SE2025★ 1 cited

High-Performance ARM-on-ARM Virtualization for Multicore SystemC-TLM-Based Virtual Platforms

Nils Bosbach, Rebecca Pelke, Niko Zurstraßen +3

The increasing complexity of hardware and software requires advanced development and test methodologies for modern systems on chips. This paper presents a novel approach to ARM-on-…

cs.ET2025

Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer

Rebecca Pelke, José Cubero-Cascante, Nils Bosbach +5

Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the von Neumann bottleneck. Due to non-idea…

cs.AR2025

Introducing Instruction-Accurate Simulators for Performance Estimation of Autotuning Workloads

Rebecca Pelke, Nils Bosbach, Lennart M. Reimann +1

Accelerating Machine Learning (ML) workloads requires efficient methods due to their large optimization space. Autotuning has emerged as an effective approach for systematically ev…

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…

eess.SP2024

A Calibratable Model for Fast Energy Estimation of MVM Operations on RRAM Crossbars

José Cubero-Cascante, Arunkumar Vaidyanathan, Rebecca Pelke +3

The surge in AI usage demands innovative power reduction strategies. Novel Compute-in-Memory (CIM) architectures, leveraging advanced memory technologies, hold the potential for si…

cs.ET2024

A Fully Automated Platform for Evaluating ReRAM Crossbars

Rebecca Pelke, Felix Staudigl, Niklas Thomas +6

Resistive Random Access Memory (ReRAM) is a promising candidate for implementing Computing-in-Memory (CIM) architectures and neuromorphic circuits. ReRAM cells exhibit significant…

cs.AR2024

CLSA-CIM: A Cross-Layer Scheduling Approach for Computing-in-Memory Architectures

Rebecca Pelke, Jose Cubero-Cascante, Nils Bosbach +3

The demand for efficient machine learning (ML) accelerators is growing rapidly, driving the development of novel computing concepts such as resistive random access memory (RRAM)-ba…