From the 1 of 10 linked papers with an AI index.
10 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…
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…
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…
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…
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…