3 papers
cs.LG2025
Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications
Jelin Leslin, Martin Trapp, Martin Andraud
Neurosymbolic AI (NSAI) has recently emerged to mitigate limitations associated with deep learning (DL) models, e.g. quantifying their uncertainty or reason with explicit rules. He…
cs.AR2025
Acore-CIM: build accurate and reliable mixed-signal CIM cores with RISC-V controlled self-calibration
Omar Numan, Gaurav Singh, Kazybek Adam +6
Developing accurate and reliable Compute-In-Memory (CIM) architectures is becoming a key research focus to accelerate Artificial Intelligence (AI) tasks on hardware, particularly D…
cs.LG2024
On Hardware-efficient Inference in Probabilistic Circuits
Lingyun Yao, Martin Trapp, Jelin Leslin +4
Probabilistic circuits (PCs) offer a promising avenue to perform embedded reasoning under uncertainty. They support efficient and exact computation of various probabilistic inferen…