System Modeling

About Unconventional

Since 2022, AI has entered the mainstream, reshaping entire industries from education and software development to fundamental consumer behaviors. This revolution has created an unprecedented demand for computation - a demand that is now fundamentally limited by energy, not just in the datacenter, but at a global scale.

At Unconventional, our mission is to solve this. We are rethinking computing from the ground up to build a new foundation for AI that is 1000x more efficient. We're doing this by exploiting the rich physics of semiconductors, mapping neural networks directly to the device physics rather than relying on layers of inefficient abstraction.

The Role

As a Member of Technical Staff, System Modeling (Performance Models), you will be part of a hands-on R&D team building simulation frameworks that enable evaluation and rapid iteration across all layers of unconventional physics-based computing systems for machine learning workloads. “Extreme co-design” is our guiding principle.

System Modeling is a multi-disciplinary effort, and the team we’re building reflects that. The role involves development of physics-based system models, GPU-accelerated ML system simulations, and cross-layer system integration. You don’t need to be an expert in all of these, but you have to be very strong in at least one, and solid in the rest.

Responsibilities

You will be responsible for one or more of the following tasks:

• Building extensible and composable high-fidelity power, performance and area estimation tools for novel AI acceleration system architectures to enable rapid design space exploration.
• Define and create comparative analyses across candidate architectures and existing state-of-art implementations.
• Working with other teams to understand their needs for such modeling and simulation to support high level system design as well as lower level verification of hardware.

Minimum Qualifications

• Education

• MS/PhD in a quantitative field (AI/ML, Computer Science, Physics, Electrical Engineering, Applied Math), or BS with substantial, clear evidence of equivalent research/engineering depth.

• Performance Modeling Knowledge

• Experience with tools and development for power profiling, modeling and simulation for AI workloads.

• Deep understanding of spatial architectures and data orchestration mechanisms 

• Deep understanding of different dataflow strategies and their tradeoffs, e.g. Weight-Stationary (WS), Output-Stationary (OS), Input-Stationary (IS) and Row-Stationary (RS).