5 citations · 8 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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-…
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…
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…
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…
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…
Mapping of CNNs on multi-core RRAM-based CIM architectures
Rebecca Pelke, Nils Bosbach, Jose Cubero +3
RRAM-based multi-core systems improve the energy efficiency and performance of CNNs. Thereby, the distributed parallel execution of convolutional layers causes critical data depend…
NISTT: A Non-Intrusive SystemC-TLM 2.0 Tracing Tool
Nils Bosbach, Lukas Jünger, Jan Moritz Joseph +1
The increasing complexity of systems-on-a-chip requires the continuous development of electronic design automation tools. Nowadays, the simulation of systems-on-a-chip using virtua…