A new neural network architecture is proposed that can be used to convert Mel spectrograms into an audio signal. The architecture is designed from the ground up to be run on a mobile device, taking advantage of operators that can be parallelized easily on mobile CPUs and GPUs, being fully convolutional and non-autoregressive. It introduces a lightweight combination of a nearest neighbor resize and separable convolution as its upsampling block, that provides fast upsampling with minimal checkerboarding artifacts. The model is trained as a GAN and demonstrates stable training behavior. A method for evaluating the performance characteristics of neural vocoders on mobile devices is also described. The model is shown to be able to run at up to 20x faster than real-time on a current generation mobile CPU and up to 65x faster than realtime on a current generation mobile GPU, while being neutral or better in quality when evaluated against a comparably sized WaveRNN model.

Developing file systems from scratch is difficult and error prone. Using layered, or stackable, file systems is a powerful technique to incrementally extend the functionality of existing file systems on commodity OSes at runtime. In this article, we analyze the evolution of layering from historical models to what is found in four different present day commodity OSes: Solaris, FreeBSD, Linux, and Microsoft Windows. We classify layered file systems into five types based on their functionality and identify the requirements that each class imposes on the OS. We then present five major design issues that we encountered during our experience of developing over twenty layered file systems on four OSes. We discuss how we have addressed each of these issues on current OSes, and present insights into useful OS and VFS features that would provide future developers more versatile solutions for incremental file system development.

Operating systems are complex and their behavior depends on many factors. Source code, if available, does not directly help understand the OS's behavior, as the behavior depends on actual workloads and external inputs. Runtime profiling is a key technique for understanding the behavior and mutual-influence of modern OS components. Such profiling is useful to prove new concepts, debug problems, and optimize the performance of existing OSs. Unfortunately, existing profiling methods lack in important areas: they do not provide much of the necessary information about the OS's behavior; they require OS modification and therefore are not portable; or they exact high overheads thus perturbing the profiled OS.