TILOS is developing learning-enabled optimization techniques that bridge discrete and continuous optimization, enabling advancements in chip design, robotics, and communication networks. The initiative addresses the challenges of dynamic decision-making under uncertainty and nonconvex optimization in deep learning, enhancing efficiency in critical technology sectors.
Funding
$20M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many optimization problems in chip design, robotics, and communication networks involve both discrete and continuous variables, making them difficult to solve efficiently with traditional methods. Dynamic decision-making under uncertainty and nonconvex optimization in deep learning further compound these challenges.
Solution
TILOS is developing learning-enabled optimization techniques to bridge discrete and continuous optimization, enabling advancements in various technology sectors. Their research focuses on distributed, parallel, and federated optimization, optimization on manifolds, dynamic decisions under uncertainty, and nonconvex optimization in deep learning. By pioneering these learning-enabled optimizations, TILOS aims to transform chip design, robotics, communication networks, and other critical domains.
Target Audience
The primary audience includes researchers and practitioners in chip design, robotics, communication networks, and related fields who seek advanced optimization techniques to improve efficiency and performance.
Features
- Learning-enabled optimization techniques for bridging discrete and continuous optimization problems.
- Research into distributed, parallel, and federated optimization methods.
- Development of optimization techniques on manifolds.
- Approaches for dynamic decision-making under uncertainty.
- Nonconvex optimization methods for deep learning applications.