5500u one AMD Ryzen 5 5500U testing with a NB01 NL5xNU (1.07.11RTR1 BIOS) and AMD Lucienne 512MB on Ubuntu 22.04 via the Phoronix Test Suite. A: Processor: AMD Ryzen 5 5500U @ 2.10GHz (6 Cores / 12 Threads), Motherboard: NB01 NL5xNU (1.07.11RTR1 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: Samsung SSD 970 EVO Plus 500GB, Graphics: AMD Lucienne 512MB (1800/400MHz), Audio: AMD Renoir Radeon HD Audio, Network: Realtek RTL8111/8168/8411 + Intel Wi-Fi 6 AX200 OS: Ubuntu 22.04, Kernel: 5.18.8-051808-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.46), Vulkan: 1.2.204, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080 B: Processor: AMD Ryzen 5 5500U @ 2.10GHz (6 Cores / 12 Threads), Motherboard: NB01 NL5xNU (1.07.11RTR1 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: Samsung SSD 970 EVO Plus 500GB, Graphics: AMD Lucienne 512MB (1800/400MHz), Audio: AMD Renoir Radeon HD Audio, Network: Realtek RTL8111/8168/8411 + Intel Wi-Fi 6 AX200 OS: Ubuntu 22.04, Kernel: 5.18.8-051808-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.46), Vulkan: 1.2.204, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080 C: Processor: AMD Ryzen 5 5500U @ 2.10GHz (6 Cores / 12 Threads), Motherboard: NB01 NL5xNU (1.07.11RTR1 BIOS), Chipset: AMD Renoir/Cezanne, Memory: 16GB, Disk: Samsung SSD 970 EVO Plus 500GB, Graphics: AMD Lucienne 512MB (1800/400MHz), Audio: AMD Renoir Radeon HD Audio, Network: Realtek RTL8111/8168/8411 + Intel Wi-Fi 6 AX200 OS: Ubuntu 22.04, Kernel: 5.18.8-051808-generic (x86_64), Desktop: GNOME Shell 42.2, Display Server: X Server + Wayland, OpenGL: 4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.46), Vulkan: 1.2.204, Compiler: GCC 11.2.0, File-System: ext4, Screen Resolution: 1920x1080 AOM AV1 3.5 Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 3.48 |===================================================================== B . 3.48 |===================================================================== C . 3.46 |===================================================================== AOM AV1 3.5 Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 18.08 |==================================================================== B . 18.12 |==================================================================== C . 17.83 |=================================================================== AOM AV1 3.5 Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 6.00 |===================================================================== B . 5.93 |==================================================================== C . 5.86 |=================================================================== AOM AV1 3.5 Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 27.65 |==================================================================== B . 27.77 |==================================================================== C . 27.28 |=================================================================== AOM AV1 3.5 Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 38.92 |=================================================================== B . 39.25 |==================================================================== C . 38.53 |=================================================================== AOM AV1 3.5 Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K Frames Per Second > Higher Is Better A . 39.43 |==================================================================== B . 39.70 |==================================================================== C . 39.13 |=================================================================== AOM AV1 3.5 Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 9.24 |===================================================================== B . 9.13 |==================================================================== C . 8.88 |================================================================== AOM AV1 3.5 Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 41.70 |==================================================================== B . 40.17 |================================================================== C . 37.64 |============================================================= AOM AV1 3.5 Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 20.20 |==================================================================== B . 20.04 |=================================================================== C . 18.65 |=============================================================== AOM AV1 3.5 Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 81.65 |==================================================================== B . 81.44 |==================================================================== C . 77.35 |================================================================ AOM AV1 3.5 Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 90.33 |============================================================== B . 99.07 |==================================================================== C . 92.53 |================================================================ AOM AV1 3.5 Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p Frames Per Second > Higher Is Better A . 94.89 |=============================================================== B . 99.18 |================================================================== C . 100.39 |=================================================================== Y-Cruncher 0.7.10.9513 Pi Digits To Calculate: 1B Seconds < Lower Is Better A . 93.50 |================================================================ B . 96.18 |================================================================== C . 99.70 |==================================================================== oneDNN 2.7 Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 9.02313 |================================================================== B . 8.97234 |================================================================== C . 8.95590 |================================================================== oneDNN 2.7 Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 11.03 |============================================================== B . 11.89 |=================================================================== C . 12.02 |==================================================================== oneDNN 2.7 Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 3.56506 |======================================================= B . 4.24293 |================================================================== C . 4.24901 |================================================================== oneDNN 2.7 Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 3.32798 |================================================================= B . 3.39842 |================================================================== C . 3.39343 |================================================================== oneDNN 2.7 Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 21.99 |================================================================= B . 22.03 |================================================================= C . 23.00 |==================================================================== oneDNN 2.7 Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 13.12 |============================================================= B . 14.12 |================================================================== C . 14.62 |==================================================================== oneDNN 2.7 Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 10.77 |=========================================================== B . 12.13 |=================================================================== C . 12.40 |==================================================================== oneDNN 2.7 Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 24.50 |=================================================================== B . 24.73 |==================================================================== C . 24.74 |==================================================================== oneDNN 2.7 Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 5.33814 |============================================================ B . 5.79887 |================================================================= C . 5.90590 |================================================================== oneDNN 2.7 Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 7.47952 |========================================================== B . 8.33564 |================================================================= C . 8.52204 |================================================================== oneDNN 2.7 Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 5897.92 |============================================================= B . 6167.97 |================================================================ C . 6350.35 |================================================================== oneDNN 2.7 Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 3665.02 |============================================================== B . 3829.07 |================================================================= C . 3886.34 |================================================================== oneDNN 2.7 Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 5904.18 |============================================================ B . 6325.63 |================================================================ C . 6482.60 |================================================================== oneDNN 2.7 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better oneDNN 2.7 Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 3651.55 |============================================================== B . 3841.34 |================================================================== C . 3862.53 |================================================================== oneDNN 2.7 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU ms < Lower Is Better A . 6.00622 |================================================================== B . 6.03729 |================================================================== C . 6.03999 |================================================================== oneDNN 2.7 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 5920.71 |============================================================= B . 6285.69 |================================================================= C . 6416.94 |================================================================== oneDNN 2.7 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better A . 3669.20 |============================================================== B . 3854.23 |================================================================= C . 3889.16 |================================================================== oneDNN 2.7 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU ms < Lower Is Better A . 3.68008 |================================================================= B . 3.71180 |================================================================== C . 3.71583 |================================================================== oneDNN 2.7 Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better