Bern2Edge: A Neurosymbolic Compiler for Edge Deployment via Bernstein Polynomial Networks

2026-08-20

AI Summary

为解决边缘设备上部署高精度神经网络的问题，提出Bern2Edge框架，通过伯恩斯坦多项式激活转换预训练模型，实现高效硬件部署和可解释性。

Abstract

Deploying high-accuracy neural networks on resource-constrained edge devices remains challenging, as existing approaches treat training, compression, and hardware synthesis as separate stages, leaving a gap between software-trained models and efficient end-to-end deployment with limited support for interpretability. We propose Bern2Edge, an end-to-end framework that uses knowledge distillation to convert a pretrained teacher feed-forward network into hardware-efficient representations via Bernstein polynomial activations. This representation enables two deployment paths: (i) a high-fidelity LUT-based realization that preserves model fidelity under compression, and (ii) a symbolic rule-based representation derived from Bernstein activation geometry, enabling interpretable inference with explicit input-space constraints. The resulting BNNs achieve up to 2.12 percentage-point (pp) accuracy improvement over ReLU under identical compression constraints. At the system level, Bern2Edge achieves up to 99.8% latency reduction and 95.2% BRAM reduction relative to a W8A8 quantized teacher on an AMD Xilinx KV260 FPGA, while maintaining accuracy within 0.5 pp, and further deploys on a low-power Spartan-7 XC7S15 FPGA. The rule-based path reduces DSP usage by up to 89.0% at a cost of 1.5 pp in total accuracy.

Problem

• neural networks
• edge devices
• resource-constrained
• end-to-end deployment
• interpretability

Innovation

• Neurosymbolic Compiler
• Bernstein Polynomial Networks
• Knowledge Distillation
• Hardware-Efficient Representation
• Interpretable Inference