2 citations · 3 across the 13 of their papers we have counts for
13 papers
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…
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…
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…
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…
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…
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…
QTFlow: Quantitative Timing-Sensitive Information Flow for Security-Aware Hardware Design on RTL
Lennart M. Reimann, Anshul Prashar, Chiara Ghinami +4
In contemporary Electronic Design Automation (EDA) tools, security often takes a backseat to the primary goals of power, performance, and area optimization. Commonly, the security…
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…
Fault Injection in Native Logic-in-Memory Computation on Neuromorphic Hardware
Felix Staudigl, Thorben Fetz, Rebecca Pelke +4
Logic-in-memory (LIM) describes the execution of logic gates within memristive crossbar structures, promising to improve performance and energy efficiency. Utilizing only binary va…