PassiveLogic’s truly autonomous control of buildings and more is enabled by the use of physics-based digital twin simulations. To match these simulations to the real world and use them to make optimal control decisions, we need to be able to run these simulations faster than real-time. Additionally, we need to be able to optimize them at the edge, not on massive servers. Differentiable programming and gradient descent optimization are the key items needed to make this possible.

Recently, members of the PassiveLogic compiler team had the opportunity to attend the 2023 LLVM Developers’ Meeting in Santa Clara, CA. It was a tremendously rewarding experience for all of us.

For the vast majority of programmers, when our code doesn’t build or doesn’t run correctly it’s almost always our fault. The compiler is pretty much the last thing we blame. However, at PassiveLogic, we push the limits of the Swift programming language, in particular by using an experimental language feature and helping in its evolution. As a result, “my code broke the compiler” is something you hear a little more frequently around here.

Over the past year, significant work has gone into identifying, reproducing, and fixing bugs in differentiable Swift that stood in the way of specific applications. As a result of that effort, we at PassiveLogic are now able to deploy all of our Swift-based simulation and control software into production. This is an important milestone for us on our way to deploy truly autonomous control systems for buildings.