Technical details of ML.NET

With the release of ML.NET 1.4, the targeting of .NET Core 3.0 or later is recommended to take advantage of the hardware intrinsics added as part of .NET Core 3.0. For those unfamiliar, .NET Core 2.x (and earlier) along with .NET Framework are optimized for CPUs with Streaming SIMD Extensions (SSE). Effectively, these instructions provide an optimized path for performing several CPU instructions on a dataset. This approach is referred to as Single Instruction Multiple Data (SIMD). Given that the SSE CPU extensions were first added in the Pentium III back in 1999 and later added by AMD in the Athlon XP in 2001, this has provided an extremely backward-compatible path. However, this also does not allow code to take advantage of all the advancements made in CPU extensions made in the last 20 years. One such advancement is the Advanced Vector Extensions (AVX) available on most Intel and AMD CPUs created in 2011 or later.

This provides eight 32-bit operations in a single instruction, compared to the four SSE provides. As you can probably guess, machine learning can take advantage of this doubling of instructions. For CPUs in .NET Core 3 that are not supported yet (such as ARM), .NET Core 3 automatically falls back to a software-based implementation.