With the arrival of Zen 4, however, the tables are turning on this one. Upscaling with AI from Topaz Labs (source: Intel) Previously an advantage for Intel, now for the competition At the time, the advantage over competing processors without VNNI was significant. 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…).Īnd 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. They should use 16-bit and 8-bit precision (with integer values), which are useful for inference, i.e.
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. 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 and Tiger Lake processors (10th and 11th generation Core for laptops).
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. You may have heard of VNNI (Vector Neural Network Instructions) before under the name DL Boost. It seems to bring huge performance improvements in a number of apps, despite the limited 256-bit width of Zen 4 SIMD units. 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. We’ve already discussed their benefits (bigger or smaller) here. Ryzen 7000 with Zen 4 architecture is the first AMD processor to support 512-bit AVX-512 vector instructions.
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