MKL-DNN DNNL Ryzen 7 2700D
AMD Ryzen 7 2700X Eight-Core testing with a ASUS ROG CROSSHAIR VII HERO (WI-FI) (1201 BIOS) and Sapphire AMD Radeon RX 470/480 on Debian 10 via the Phoronix Test Suite.


2700x: 

	Processor: AMD Ryzen 7 2700X Eight-Core @ 3.70GHz (8 Cores / 16 Threads), Motherboard: ASUS ROG CROSSHAIR VII HERO (WI-FI) (1201 BIOS), Chipset: AMD 17h, Memory: 16384MB, Disk: Samsung SSD 970 EVO 250GB, Graphics: Sapphire AMD Radeon RX 470/480, Audio: AMD Ellesmere HDMI Audio, Network: Intel I211 + Realtek RTL8822BE 802.11a/b/g/n/ac

	OS: Debian 10, Kernel: 4.19.0-5-amd64 (x86_64), Display Server: X Server 1.20.4, Display Driver: modesetting 1.20.4, Compiler: GCC 8.3.0, File-System: ext4, Screen Resolution: 1024x768


MKL-DNN DNNL 1.1
Harness: IP Batch 1D - Data Type: f32
ms < Lower Is Better
2700x . 12.26 |================================================================


MKL-DNN DNNL 1.1
Harness: IP Batch All - Data Type: f32
ms < Lower Is Better
2700x . 29.75 |================================================================


MKL-DNN DNNL 1.1
Harness: IP Batch 1D - Data Type: u8s8f32
ms < Lower Is Better
2700x . 235.46 |===============================================================


MKL-DNN DNNL 1.1
Harness: IP Batch All - Data Type: u8s8f32
ms < Lower Is Better
2700x . 1071.13 |==============================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_3d - Data Type: f32
ms < Lower Is Better
2700x . 36.00 |================================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_all - Data Type: f32
ms < Lower Is Better
2700x . 5665.28 |==============================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_3d - Data Type: u8s8f32
ms < Lower Is Better
2700x . 13971.63 |=============================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_1d - Data Type: f32
ms < Lower Is Better
2700x . 14.20 |================================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_3d - Data Type: f32
ms < Lower Is Better
2700x . 12.97 |================================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_alexnet - Data Type: f32
ms < Lower Is Better
2700x . 716.01 |===============================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_all - Data Type: u8s8f32
ms < Lower Is Better
2700x . 58919.30 |=============================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_all - Data Type: f32
ms < Lower Is Better
2700x . 5168.76 |==============================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_1d - Data Type: u8s8f32
ms < Lower Is Better
2700x . 4675.79 |==============================================================


MKL-DNN DNNL 1.1
Harness: Deconvolution Batch deconv_3d - Data Type: u8s8f32
ms < Lower Is Better
2700x . 8187.05 |==============================================================


MKL-DNN DNNL 1.1
Harness: Recurrent Neural Network Training - Data Type: f32
ms < Lower Is Better
2700x . 499.21 |===============================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_alexnet - Data Type: u8s8f32
ms < Lower Is Better
2700x . 7612.37 |==============================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_googlenet_v3 - Data Type: f32
ms < Lower Is Better
2700x . 318.72 |===============================================================


MKL-DNN DNNL 1.1
Harness: Convolution Batch conv_googlenet_v3 - Data Type: u8s8f32
ms < Lower Is Better
2700x . 2729.38 |==============================================================