Showing cs.ARShow all
2 papers · 1 filter
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…