Simulation is a coinductive proof technique to assert the behavioral equivalence of computing systems that has seen fruitful application in distributed systems, concurrent process calculi, and programming languages, since the 1970’s. We have also utilized simulation in our prior work, where we formalized and proved a folklore claim that the state-based and operation-based approaches to […]

In this work, we develop numerical methods for conservation laws that explore statistical, structure-preserving, and machine-learning-based approaches, each built on top of traditional numerical solvers. First, we develop a general Gaussian-process-based “recipe’’ for constructing high-order linear operators such as interpolation, reconstruction, and derivative approximations. Building on this recipe, we derive a kernel-agnostic convergence theory for […]

Hardware design flows have become increasingly complex as modern chips integrate billions of transistors and rely on aggressive synthesis optimizations to meet performance, area, and power targets. While these transformations […]

Modern applications, such as data center workloads, have become increasingly complex. These applications primarily operate on massive datasets, which involve large memory footprints, irregular access patterns, and complex control and […]

Reinforcement Learning (RL) is a machine learning paradigm that trains a decision maker, or policy, by learning from interaction with an environment. The power of RL lies in its ability […]