3 papers
cs.AR2026
Compiling Multi-Threshold Operators for Quantized Non-Monotonic and Fine-Granular Activations in Custom-Tailored DNN Accelerators
Christoph Berganski, Felix Jentzsch, Marco Platzner
Aggressive quantization of neural networks can expose activation functions as bottlenecks for efficient hardware acceleration. Implementing quantized activations as multi-threshold…
cs.AR2026
Sensitivity-Guided Framework for Pruned and Quantized Reservoir Computing Accelerators
Atousa Jafari, Mahdi Taheri, Hassan Ghasemzadeh Mohammadi +2
This paper presents a compression framework for Reservoir Computing that enables systematic design-space exploration of trade-offs among quantization levels, pruning rates, model a…
cs.RO2023
AutonomROS: A ReconROS-based Autonomous Driving Unit
Christian Lienen, Mathis Brede, Daniel Karger +7
Autonomous driving has become an important research area in recent years, and the corresponding system creates an enormous demand for computations. Heterogeneous computing platform…