ARM64 oneDNN 2.0
Ampere Altra ARMv8 Neoverse-N1 testing with a WIWYNN Mt.Jade (1.1.20201019 BIOS) and ASPEED on Ubuntu 20.10 via the Phoronix Test Suite.


1: 

	Processor: Ampere Altra ARMv8 Neoverse-N1 @ 3.30GHz (160 Cores), Motherboard: WIWYNN Mt.Jade (1.1.20201019 BIOS), Chipset: Ampere Computing LLC Device e100, Memory: 502GB, Disk: 3841GB Micron_9300_MTFDHAL3T8TDP + 960GB SAMSUNG MZ1LB960HAJQ-00007, Graphics: ASPEED, Monitor: VE228, Network: Mellanox MT28908 + Intel I210

	OS: Ubuntu 20.10, Kernel: 5.10.0-051000rc6daily20201206-generic (aarch64) 20201206, Display Server: X Server 1.20.9, Display Driver: modesetting 1.20.9, Compiler: GCC 10.2.0, File-System: ext4, Screen Resolution: 1920x1080


oneDNN 2.0
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 55.69 |====================================================================


oneDNN 2.0
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 121.39 |===================================================================


oneDNN 2.0
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 82.24 |====================================================================


oneDNN 2.0
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 179.47 |===================================================================


oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 143.52 |===================================================================


oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 34.67 |====================================================================


oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 34.72 |====================================================================


oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 65.94 |====================================================================


oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 20.74 |====================================================================


oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 35.54 |====================================================================


oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 16960.1 |==================================================================


oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 16887.1 |==================================================================


oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 15974.1 |==================================================================


oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
1 . 16839.0 |==================================================================


oneDNN 2.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
1 . 17.23 |====================================================================