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.
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 by using an experimental language feature and helping in its evolution.
Let’s get a feel for how to use it beyond a trivial example. In order to do so, we’ll need to understand a few things about how it works.
So now we understand how to optimize a function with Gradient Descent, as long as we can get the derivative of the function. Great, if all functions had an obvious derivative, we would be able to optimize everything!
Automatic differentiation is an exciting emerging technology which enables deep learning applications and is of particular value to PassiveLogic’s smart building platform.
In Part 0 of this series, we introduce the usefulness of automatic differentiation.
What if code could run, not just forwards, but backwards too? While automatic differentiation isn’t exactly reversible computing, it’s a close proxy for solving the large number of computing problems that take the form of: “I have the answer… what was the question?”
At PassiveLogic we undertook an interesting experiment early in 2016 to refactor some of our C-code to Apple’s new systems language Swift. Then, we decided to make the bold move of rewriting our whole code-base in Swift.
