Late Breaking Results: Conversion of Neural Networks into Logic Flows for Edge Computing

2026-01-29

AI Summary

This work addresses the inefficiency of multiply-accumulate (MAC) operations that bottleneck neural network inference on resource-constrained edge-device CPUs. To overcome this limitation, the authors propose a novel approach that equivalently transforms a neural network into a decision tree, extracts decision paths leading to constant leaf nodes, and compresses them into a control-flow-dominated logic structure composed primarily of if-else statements. This transformation effectively bypasses the majority of MAC computations and represents the first efficient conversion from dataflow-centric neural networks to control-flow-centric logical programs—aligning naturally with CPU execution characteristics. Evaluated on a RISC-V CPU simulator, the method achieves up to a 14.9% reduction in inference latency while preserving model accuracy exactly.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Neural networks have been successfully applied in various resource-constrained edge devices, where usually central processing units (CPUs) instead of graphics processing units exist due to limited power availability. State-of-the-art research still focuses on efficiently executing enormous numbers of multiply-accumulate (MAC) operations. However, CPUs themselves are not good at executing such mathematical operations on a large scale, since they are more suited to execute control flow logic, i.e., computer algorithms. To enhance the computation efficiency of neural networks on CPUs, in this paper, we propose to convert them into logic flows for execution. Specifically, neural networks are first converted into equivalent decision trees, from which decision paths with constant leaves are then selected and compressed into logic flows. Such logic flows consist of if and else structures and a reduced number of MAC operations. Experimental results demonstrate that the latency can be reduced by up to 14.9 % on a simulated RISC-V CPU without any accuracy degradation. The code is open source at https://github.com/TUDa-HWAI/NN2Logic

Problem

• neural networks
• edge computing
• CPU efficiency
• multiply-accumulate operations
• logic flows

Innovation

• logic flow
• neural network conversion
• edge computing
• decision tree
• CPU acceleration