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Towards Hardware-Aware Tractable Learning of Probabilistic Models
Laura Galindez Olascoaga · Wannes Meert · Nimish Shah · Marian Verhelst · Guy Van den Broeck

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #126

Smart portable applications increasingly rely on edge computing due to privacy and latency concerns. But guaranteeing always-on functionality comes with two major challenges: heavily resource-constrained hardware; and dynamic application conditions. Probabilistic models present an ideal solution to these challenges: they are robust to missing data, allow for joint predictions and have small data needs. In addition, ongoing efforts in field of tractable learning have resulted in probabilistic models with strict inference efficiency guarantees. However, the current notions of tractability are often limited to model complexity, disregarding the hardware's specifications and constraints. We propose a novel resource-aware cost metric that takes into consideration the hardware's properties in determining whether the inference task can be efficiently deployed. We use this metric to evaluate the performance versus resource trade-off relevant to the application of interest, and we propose a strategy that selects the device-settings that can optimally meet users' requirements. We showcase our framework on a mobile activity recognition scenario, and on a variety of benchmark datasets representative of the field of tractable learning and of the applications of interest.

Author Information

Laura Galindez Olascoaga (KU Leuven)

I am a PhD candidate at the Microelectronics and sensors (MICAS) lab of KU Leuven, Belgium. My research consists on the development of hardware-aware machine learning frameworks and algorithms, which are meant to exploit the hardware devices they run in optimally.

Wannes Meert (K.U.Leuven)
Nimish Shah (KU Leuven)
Marian Verhelst (KU Leuven)
Guy Van den Broeck (UCLA)

I am an Assistant Professor and Samueli Fellow at UCLA, in the Computer Science Department, where I direct the Statistical and Relational Artificial Intelligence (StarAI) lab. My research interests are in Machine Learning (Statistical Relational Learning, Tractable Learning), Knowledge Representation and Reasoning (Graphical Models, Lifted Probabilistic Inference, Knowledge Compilation), Applications of Probabilistic Reasoning and Learning (Probabilistic Programming, Probabilistic Databases), and Artificial Intelligence in general.

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