Publicaties
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Dynamic Complexity Tuning for Hardware-Aware Probabilistic Circuits KU Leuven
Probabilistic inference is a well suited approach to address the challenges of resource constrained embedded application scenarios. In particular, probabilistic models learned generatively are robust to missing data and are capable of encoding domain knowledge seamlessly. These traits have been leveraged to propose hardware-aware probabilistic learning and inference strategies that induce Pareto optimal accuracy versus resource consumption ...
Processor Architecture Optimization for Spatially Dynamic Neural Networks KU Leuven
Spatially dynamic neural networks adjust network execution based on the input data, saving computations by skipping non-important image regions. Yet, GPU implementations fail to achieve speedups from these spatially dynamic execution patterns for most neural network architectures. This paper investigates hardware constraints preventing such speedup and proposes and compares novel processor architectures and dataflows enabling latency ...
Flexible, Self-adaptive Sense-and-Compress SoC for sub-microWatt always-on sensory recording KU Leuven
We present a 5-sensor, fully integrated sensing system with interchangeable sensors and programmable configuration to create a sub-microWatt multisensor node that can tackle a wide range of sensing applications. Furthermore, the sensor node is capable of autonomously adapting its configuration to the application requirements hence minimizing system power. Such self-reconfiguration is enabled at low overhead by developing an automated offline ...
Binary CorNET: Accelerator for HR Estimation From Wrist-PPG KU Leuven
Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly successful in mitigating the effect of motion artifacts (MA) in ambulatory environment for HR estimation. Recently, a learning framework, CorNET, employing two-layer convolution neural networks (CNN) ...