面向深度神经网络分割计算的基于范围非对称数字系统的轻量级中间特征压缩方法
Range Asymmetric Numeral Systems-Based Lightweight Intermediate Feature Compression for Split Computing of Deep Neural Networks
AI总结：
针对深度神经网络分割计算的中间特征传输瓶颈，提出结合rANS编码、非对称整数量化与稀疏表示的轻量级压缩框架，实现低开销高压缩率，跨CV与NLP任务验证了有效性。
摘要：
分割计算将深度神经网络推理任务分配在资源受限的边缘设备与云服务器之间，但在传输中间特征时面临严重的通信瓶颈。为此，本文提出一种新型轻量级压缩框架，结合范围非对称数字系统（rANS）编码、非对称整数量化与稀疏张量表示，大幅降低传输开销。具体而言，该方法将非对称整数量化与稀疏表示技术相结合，无需复杂的概率建模或网络修改。核心贡献包括：（1）一种与分布无关的压缩流水线，利用张量固有稀疏性实现带宽降低，且计算开销极小；（2）一种近似理论模型，可优化张量重塑维度以最大化压缩效率；（3）GPU加速实现方案，编解码延迟低于1毫秒。在ResNet、VGG16、MobileNetV2、SwinT、DenseNet121、EfficientNetB0等多种神经架构上的大量评估表明，所提框架在CIFAR100与ImageNet基准上始终保持接近基线的准确率。此外，我们通过在MMLU、HellaSwag、ARC、PIQA、Winogrande、BoolQ、OpenBookQA等标准基准上采用Llama2 7B与13B模型，验证了该框架在先进自然语言处理任务上的有效性，证明其在计算机视觉之外的广泛适用性。该方法可在不损失模型性能的前提下，解决带宽受限环境中部署复杂人工智能系统的核心瓶颈。
英文摘要：
Split computing distributes deep neural network inference between resource-constrained edge devices and cloud servers but faces significant communication bottlenecks when transmitting intermediate features. To this end, in this paper, we propose a novel lightweight compression framework that leverages Range Asymmetric Numeral Systems (rANS) encoding with asymmetric integer quantization and sparse tensor representation to reduce transmission overhead dramatically. Specifically, our approach combines asymmetric integer quantization with a sparse representation technique, eliminating the need for complex probability modeling or network modifications. The key contributions include: (1) a distribution-agnostic compression pipeline that exploits inherent tensor sparsity to achieve bandwidth reduction with minimal computational overhead; (2) an approximate theoretical model that optimizes tensor reshaping dimensions to maximize compression efficiency; and (3) a GPU-accelerated implementation with sub-millisecond encoding/decoding latency. Extensive evaluations across diverse neural architectures (ResNet, VGG16, MobileNetV2, SwinT, DenseNet121, EfficientNetB0) demonstrate that the proposed framework consistently maintains near-baseline accuracy across CIFAR100 and ImageNet benchmarks. Moreover, we validated the framework's effectiveness on advanced natural language processing tasks by employing Llama2 7B and 13B on standard benchmarks such as MMLU, HellaSwag, ARC, PIQA, Winogrande, BoolQ, and OpenBookQA, demonstrating its broad applicability beyond computer vision. Furthermore, this method addresses a fundamental bottleneck in deploying sophisticated artificial intelligence systems in bandwidth-constrained environments without compromising model performance.
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