qnn.concatenate • qnn.add, qnn.mul • QNN operators will be lowered to Relay operators • QNN Optimization passes • Some optimizations are easier at QNN level • Intel x86 VNNI requires conv input dtypes

splits the image into smaller chunks, allowing for multi- threaded encoding, as well as per-chunk optimization. This option allows you to choose whether the encoder should do so with the lossy VarDCT method same in both, vector and scalar algorithms. Sometimes you will be tempted to do some fast-path optimization in the scalar version of the algorithm, which are not available in the vector version. Don't do really hard to find bugs in rendering. Basically, you need to always make sure that the fast-path optimization in scalar and vector algorithms are exactly the same, even if it means you have to remove some

何かを一緒にハッキングする場合は、あちこちで unwrap/expect を行うのが一般的です が、本番環境のコードは通常、None をより適切に処理します。 • The ”niche optimization” means that Option often has the same size in memory as T, if there is some representation makes Box one of the types that allow the compiler to optimize storage of some enums (the ”niche optimization”). 20.2 Rc Rc は、参照カウントされた共有ポインタです。複数の場所から同じデータを参照する必要がある場合 に使用します。 122 use std::rc::Rc; fn stack addresses and run it on the Playground or look at the assembly in Godbolt. In the ”DEBUG” optimization level, the addresses should change, while they stay the same when changing to the ”RELEASE” setting:

segment will result in KiCad optimizing the rest of the track that is visible on the screen. The optimization process removes unnecessary corners, avoids acute angles, and generally tries to find the shortest