# GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization

arXiv: https://arxiv.org/abs/2602.20427 · HTML: https://mlsanity.com/paper/2602.20427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks](https://mlsanity.com/paper/2303.08256/) (2023)
- [E-Syn: E-Graph Rewriting with Technology-Aware Cost Functions for Logic Synthesis](https://mlsanity.com/paper/2403.14242/) (2024)
- [Differentiable Combinatorial Scheduling at Scale](https://mlsanity.com/paper/2406.06593/) (2024)
- [Optimal Software Pipelining and Warp Specialization for Tensor Core GPUs](https://mlsanity.com/paper/2512.18134/) (2025)
- [GATSPI: GPU Accelerated Gate-Level Simulation for Power Improvement](https://mlsanity.com/paper/2203.06117/) (2022)

## Abstract (arXiv)

Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis. While recent differentiable approaches have sought to replace traditional ones like exact solvers or heuristics with gradient-based search, they typically rely on categorical distributions that fail to capture the ordinal nature of time and suffer from a parameter space that scales poorly. In this paper, we propose a novel differentiable framework, GauS, that models operator scheduling as a stochastic relaxation using Gaussian distributions, which fully utilize modern parallel computing devices like GPUs. By representing schedules as continuous Gaussian variables, we successfully capture the ordinal nature of time and reduce the optimization space by orders of magnitude. Our method is highly flexible to represent various objectives and constraints, which provides the first differentiable formulation for the complex pipelined scheduling problem. We evaluate our method on a range of benchmarks, demonstrating that Gaus achieves Pareto-optimal results.