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In this paper, we consider the challenges that arise from the need to scale virtualized network functions (VNFs) at 100 Gbps line speed and beyond. Traditional VNF designs are monolithic in state management and scheduling: internally maintaining all states and operations associated with them. Without proper design considerations, it suffers from limitations when scaling at 100 Gbps link speed and beyond: the inability of efficient utilization of the cache because of the contention due to the frequent control plane activities, computational/memory-intensive tasks taking up CPU times, shares states causing the synchronization among the cores. We address these limitations by arguing for the need to granularly decompose a VNF into data/control components that are co-located within a server but can be independently scaled among the cores. To realize the approach, we design a "serverless" programming framework with novel abstraction to optimize the data components that must process packets at the line speed, reduce the contention of the data states and enable run-time scheduling of different components for improved resource utilization. The abstractions, combined with the runtime system that we design, help NFV developers focus on the logic and correctness of VNF programming without worrying about how VNFs may be scaled in or out. We evaluate our platform by comparing it with monolithic approaches using different workloads and by analyzing its advantages of separation on scalability, performance determinism, and feature velocity. 

Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, gradient-based visual attention methods have driven much recent effort in using visual attention maps as a means for visual explanations. A key problem, however, is these methods are designed for classification and categorization tasks, and their extension to explaining generative models, e.g., variational autoencoders (VAE) is not trivial. In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention. We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions. We show how these attention maps can be used to localize anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD dataset. We also show how they can be infused into model training, helping bootstrap the VAE into learning improved latent space disentanglement, demonstrated on the Dsprites dataset.