InTreeger: An End-to-End Framework for Integer-Only Decision Tree Inference
Duncan Bart, Bruno Endres Forlin, Ana-Lucia Varbanescu, Marco Ottavi, Kuan-Hsun Chen

TL;DR
InTreeger is a comprehensive framework that converts decision trees into highly optimized integer-only C code, enabling efficient, precise, and hardware-agnostic inference on resource-limited devices.
Contribution
It introduces an end-to-end, architecture-agnostic method for generating integer-only decision tree implementations without loss of accuracy.
Findings
Significant reduction in inference latency across ARM, x86, RISC-V architectures.
Improved energy efficiency over floating-point decision tree implementations.
Enables deployment on ultra-low power and embedded systems.
Abstract
Integer quantization has emerged as a critical technique to facilitate deployment on resource-constrained devices. Although they do reduce the complexity of the learning models, their inference performance is often prone to quantization-induced errors. To this end, we introduce InTreeger: an end-to-end framework that takes a training dataset as input, and outputs an architecture-agnostic integer-only C implementation of tree-based machine learning model, without loss of precision. This framework enables anyone, even those without prior experience in machine learning, to generate a highly optimized integer-only classification model that can run on any hardware simply by providing an input dataset and target variable. We evaluated our generated implementations across three different architectures (ARM, x86, and RISC-V), resulting in significant improvements in inference latency. In…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning and Data Classification · Anomaly Detection Techniques and Applications · Bayesian Modeling and Causal Inference
