CO-NAS: Joint Neural Architecture Search for Compression, Quantization, and Edge–Cloud Model Offloading

The efficient deployment of deep neural networks (DNNs) on resource-constrained edge devices necessitates a delicate balance between model accuracy, communication bandwidth, and computational latency. While existing methods often address partitioning, quantization, or compression in isolation, this paper introduces CO-NAS (Joint Neural Architecture Search for Compression, Quantization, and Edge–Cloud Model Offloading), a unified framework that jointly optimizes these three critical dimensions for collaborative edge-cloud inference. By utilizing an adapted version of the stochastic architecture search (SNAS) paradigm, CO-NAS leverages Temperature-Annealed Softmax relaxations to enable gradient-based optimization over discrete choices of partition points, compression ratios, and activation bit-widths. Extensive experiments on ResNet-50 and VGG-16 across CIFAR-100 and Fashion-MNIST datasets demonstrate the framework’s efficacy. CO-NAS discovers Pareto-optimal configurations, such as a ResNet-50 model for CIFAR-100 achieving 70.1% accuracy with extreme compression (48× channel reduction and 1-bit quantization), representing a total compression of over 1500× on transmitted features. Crucially, exhaustive grid search validation confirms that CO-NAS reliably identifies top-performing architectures within the vast search space, while achieving a 21×-27× reduction in search cost compared to the exhaustive baseline. The results validate CO-NAS as a scalable and efficient approach for automating the co-design of accurate and communication-efficient neural networks for the edge-cloud paradigm.