Martin Rinard

TL;DR: The experimental results show that PowerDial can enable benchmark applications to execute responsively in the face of power caps that would otherwise significantly impair the delivered performance and reduce the number of machines required to meet peak load.

TL;DR: These results explain the great expressive power of spatial conjunction and can be used to show that adding unrestricted spatial conjunction to a decidable logic leads to an un-decidable logic.

TL;DR: PerformanceHat, a new system that uses profiling information from production executions to develop a global performance model suitable for integration into interactive development environments, is presented and results indicate that developers using PerformanceHat were significantly faster in detecting the performance problem, and finding the root-cause of the problem.

TL;DR: In this article, Bayesian inference is used to synthesize probabilistic programs for time series data and multivariate tabular data given observed data, which can accurately infer qualitative structure and outperform standard data analysis methods in forecasting and predicting new data.

Abstract: Deep neural networks are an attractive tool for compressing the control policy lookup tables in systems such as the Airborne Collision Avoidance System (ACAS). It is vital to ensure the safety of such neural controllers via verification techniques. The problem of analyzing ACAS Xu networks has motivated many successful neural network verifiers. These verifiers typically analyze the internal computation of neural networks to decide whether a property regarding the input/output holds. The intrinsic complexity of neural network computation renders such verifiers slow to run and vulnerable to floating-point error.