5 citations · 12 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
Bit Error Tolerance Metrics for Binarized Neural Networks
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen +5
To reduce the resource demand of neural network (NN) inference systems, it has been proposed to use approximate memory, in which the supply voltage and the timing parameters are tu…
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…