2 citations · 3 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
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…