3 papers
cs.CV2025
EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision
Yufeng Yang, Adrian Kneip, Charlotte Frenkel
Edge vision systems combining sensing and embedded processing promise low-latency, decentralized, and energy-efficient solutions that forgo reliance on the cloud. As opposed to con…
cs.AR2024
IMAGINE: An 8-to-1b 22nm FD-SOI Compute-In-Memory CNN Accelerator With an End-to-End Analog Charge-Based 0.15-8POPS/W Macro Featuring Distribution-Aware Data Reshaping
Adrian Kneip, Martin Lefebvre, Pol Maistriaux +1
Charge-domain compute-in-memory (CIM) SRAMs have recently become an enticing compromise between computing efficiency and accuracy to process sub-8b convolutional neural networks (C…
cs.AR2024
An Event-Based Digital Compute-In-Memory Accelerator with Flexible Operand Resolution and Layer-Wise Weight/Output Stationarity
Nicolas Chauvaux, Adrian Kneip, Christoph Posch +2
Compute-in-memory (CIM) accelerators for spiking neural networks (SNNs) are promising solutions to enable s-level inference latency and ultra-low energy in edge vision applicat…