{"id":118906,"date":"2022-10-10T18:23:37","date_gmt":"2022-10-10T16:23:37","guid":{"rendered":"https:\/\/www.hwcooling.net\/?p=118906\/"},"modified":"2022-10-10T21:41:08","modified_gmt":"2022-10-10T19:41:08","slug":"why-is-zen-4-so-fast-in-topaz-labs-ai-apps-its-intels-doing","status":"publish","type":"post","link":"https:\/\/www.hwcooling.net\/en\/why-is-zen-4-so-fast-in-topaz-labs-ai-apps-its-intels-doing\/","title":{"rendered":"Why is Zen 4 so fast in Topaz Labs AI apps? In fact it’s Intel’s doing"},"content":{"rendered":"

Ryzen 7000 with Zen 4 architecture is the first AMD processor to support 512-bit AVX-512 vector instructions. We’ve already discussed their benefits (bigger or smaller) here. But the Zen 4 cores support another instruction set extension that used to be Intel’s pride and joy, and now the roles have reversed a bit: VNNI. It seems to bring huge performance improvements in a number of apps, despite the limited 256-bit width of Zen 4 SIMD units.<\/p>\n

You may have heard of VNNI (Vector Neural Network Instructions) before under the name DL Boost. This designation subsumed the 512-bit VNNI instructions, also sometimes referred to as AVX512_VNNI, on the one hand, and support for BFloat16 (AVX512_BF16) data type operations on the other. The second extension was first featured in the Cooper Lake server Xeons, the first one (VNNI) was one of Intel’s highlights for the 10nm Ice Lake<\/a> and Tiger Lake<\/a> processors (10th and 11th generation Core for laptops).<\/p>\n

Intel promised that VNNI instructions would dramatically increase the performance of these processors in neural network operations, the “AI” applications for which these instructions are explicitly designed. They should use 16-bit and 8-bit precision (with integer values), which are useful for inference, i.e. for applying an already trained network. The company then partnered with Topaz Labs to have them use VNNI (via the OpenVINO framework) to optimize their applications (Gigapixel AI, Denoise AI, Video Enhance AI…).<\/p>\n

And Intel then showed Topaz Labs apps in their official benchmarks, where they gave the 10th\/11th generation quad-core mobile processors higher performance than they would normally get. At the time, the\u00a0 advantage over competing processors without VNNI was significant.<\/p>\n

\"\"<\/a>
Upscaling with AI from Topaz Labs (source: Intel)<\/figcaption><\/figure>\n

Previously an advantage for Intel, now for the competition<\/h3>\n

With the arrival of Zen 4, however, the tables are turning on this one. Ironically, Intel removed support for AVX512_VNNI instructions from Alder Lake processors because they use 512-bit ZMM registers and are one of the subsets of AVX-512 (albeit a very specific one). Conversely, AMD has jumped in with Zen 4 core that introduces these instructions, so now there’s a situation where the advantage is on their side.<\/p>\n

In Topaz Labs apps, we did\u00a0 observe performance that is well above the average of the Ryzen 7000 in other programs<\/a> in our reviews. The Ryzen 9 7900X was 90\u2013126 % faster than the Ryzen 9 5900X, but even the Alder Lake processors got a similar beating \u2013 against those, the Ryzen 9 7900X is 75\u201395 % faster in these tests, which isn’t really in line with results common in other benchmarks and apps. And 7900X isn’t even the most powerful model AMD has in the Zen 4 lineup. We’ll see if the Ryzen 9 7950X manages to scale even higher. However, even the hexacore Ryzen 5 7600X already shows really high performance<\/a>.<\/p>\n

Zen 4 Benchmarks: AI applications Topaz Labs<\/h2>\n