5 citations · 8 across the 12 of their papers we have counts for
6 papers · 1 filter
Jump Like A Squirrel: Optimized Execution Step Order for Anytime Random Forest Inference
Daniel Biebert, Christian Hakert, Kay Heider +3
Due to their efficiency and small size, decision trees and random forests are popular machine learning models used for classification on resource-constrained systems. In such syste…
WCDT: Systematic WCET Optimization for Decision Tree Implementations
Nils Hölscher, Christian Hakert, Georg von der Brüggen +3
Machine-learning models are increasingly deployed on resource-constrained embedded systems with strict timing constraints. In such scenarios, the worst-case execution time (WCET) o…
TREE: Tree Regularization for Efficient Execution
Lena Schmid, Daniel Biebert, Christian Hakert +4
The rise of machine learning methods on heavily resource constrained devices requires not only the choice of a suitable model architecture for the target platform, but also the opt…
Register Your Forests: Decision Tree Ensemble Optimization by Explicit CPU Register Allocation
Daniel Biebert, Christian Hakert, Kuan-Hsun Chen +1
Bringing high-level machine learning models to efficient and well-suited machine implementations often invokes a bunch of tools, e.g.~code generators, compilers, and optimizers. Al…
FLInt: Exploiting Floating Point Enabled Integer Arithmetic for Efficient Random Forest Inference
Christian Hakert, Kuan-Hsun Chen, Jian-Jia Chen
In many machine learning applications, e.g., tree-based ensembles, floating point numbers are extensively utilized due to their expressiveness. Nowadays performing data analysis on…
Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen +6
Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, t…