Radeon Instinct

Radeon Instinct is AMD's brand of deep learning oriented GPUs.[1][2] It replaced AMD's FirePro S brand in 2016. Compared to the Radeon brand of mainstream consumer/gamer products, the Radeon Instinct branded products are intended to accelerate deep learning, artificial neural network, and high-performance computing/GPGPU applications.

AMD Radeon Instinct
Design firmAdvanced Micro Devices
TypeServers

The Radeon Instinct product line directly competes with Nvidia's Tesla and Intel Xeon Phi lines of deep learning and GPGPU cards.

Products
Main articles: List of AMD workstation GPUs and List of AMD mobile workstation GPUs

The three initial Radeon Instinct products were announced in December 2016, with each based on a different architecture.

MI6

The MI6 is a passively cooled, Polaris 10 based card with 16 GB of GDDR5 memory and with a <150 W TDP.[1][2] At 5.7 TFLOPS (FP16 and FP32), the MI6 is expected to be used primarily for inference, rather than neural network training. The MI6 has a peak double precision (FP64) compute performance of 358 GFLOPS.[3]

MI8

The MI8 is a Fiji based card, analogous to the R9 Nano, and expected to have a <175W TDP.[1] The MI8 has 4 GB of High Bandwidth Memory. At 8.2 TFLOPS (FP16 and FP32), the MI8 is marked toward inference. The MI8 has a peak (FP64) double precision compute performance 512 GFLOPS.[4]

MI25

The MI25 is a Vega based card, utilizing HBM2 memory. The MI25 performance is expected to be 12.3 TFLOPS using FP32 numbers. In contrast to the MI6 and MI8, the MI25 is able to increase performance when using lower precision numbers, and accordingly is expected to reach 24.6 TFLOPS when using FP16 numbers. The MI25 is rated at <300W TDP with passive cooling. The MI25 also provides 768 GFLOPS peak double precision (FP64) at 1/16th rate.[5]

Software

MxGPU

The MI6, MI8, and MI25 products all support AMD's MxGPU virtualization technology, enabling sharing of GPU resources across multiple users.[1][6]

MIOpen

MIOpen is AMD's deep learning library to enable GPU acceleration of deep learning.[1] Much of this extends the GPUOpen's Boltzmann Initiative software.[6] This is intended to compete with the deep learning portions of Nvidia's CUDA library. It supports the deep learning frameworks: Theano, Caffe, TensorFlow, MXNet, The Microsoft Cognitive Toolkit, Torch, and Chainer. Programming is supported in OpenCL and Python, in addition to supporting the compilation of CUDA through AMD's Heterogeneous-compute Interface for Portability and Heterogeneous Compute Compiler.

Chipset Table
Model
(codename)		 Release Date Architecture
& Fab		 Transistors
& Die Size		 Core Fillrate[lower-alpha 1][lower-alpha 2][lower-alpha 3] Processing power[lower-alpha 1][lower-alpha 4]
(GFLOPS)		 Memory TBP Bus
interface
Config[lower-alpha 5] Clock[lower-alpha 1] (MHz) Texture
(GT/s)		 Pixel
(GP/s)		 Half Single Double Bus type
& width		 Size
(GiB)		 Clock
(MT/s)		 Bandwidth
(GB/s)
Radeon Instinct MI6
(Polaris 10) [1][7][8][9][10]		 December 2016 GCN 4th gen
14 nm		 5.7×109
232 mm2		 2304:144:32
36 CU		 1120
1233		 177.6 39.46 5800 5800 358 GDDR5
256-bit		 16 7000 224 150 W PCIe 3.0 x16
Radeon Instinct MI8
(Fiji XT) [1][7][8][11][12]		 GCN 3rd gen
28 nm		 8.9×109
596 mm2		 4096:256:64
64 CU		 1000 256.0 64.0 8200 8200 512 HBM
4096-bit		 4 1000 512 175 W
Radeon Instinct MI25
(Vega 10 XT)[1][7][8][13][14][15]		 GCN 5th gen
14 nm		 12.5×109
510 mm2		 4096:256:64
64 CU		 1400
1500		 384 96.0 24600 12300 768 HBM2
2048-bit		 16 1704 436.2 300 W
Radeon Instinct MI25 mxgpu
(Prototype, June 2017)[16]		 Unreleased 2×
12.5×109
510 mm2		 2×
4096:256:64
64 CU		 1400
1500		 2×
384		 2×
96.0		 2×
24600		 2×
12300		 2×
768		 2×
16		 2×
436.2		 300 W
Radeon Instinct MI50
(Vega 20 GL)[17][18][19][20]		 November 2018 GCN 5th gen
7 nm		 13.2×109
331 mm2		 3840:240:-
60 CU		 1450
1746		 419.04 - 26800 13400 6700 HBM2
4096-bit		 16 2000 1024 300 W PCIe 4.0 x16
Radeon Instinct MI60
(Vega 20 GL)[17][21][22]		 4096:256:-
64 CU		 1500
1800		 460.8 - 29450 14725 7362.5 32 1024 300 W
  1. Boost values (if available) are stated below the base value in italic.
  2. Texture fillrate is calculated as the number of texture mapping units multiplied by the base (or boost) core clock speed.
  3. Pixel fillrate is calculated as the number of render output units multiplied by the base (or boost) core clock speed.
  4. Precision performance is calculated from the base (or boost) core clock speed based on a FMA operation.
  5. Unified Shaders : Texture Mapping Units : Render Output Units and Compute Units (CU)
gollark: (Although that breaks horribly too, since you're incentivized to run 10000 middle nodes which funnel money to you for your own connection)
gollark: (I'm assuming that you pay a fixed amount per packet or something which is split between all the nodes participating in sending that)
gollark: If I am paid for being a middle node, I would get money for going between A and B with a jammer, so that B can't connect with A directly, and providing a new node.
gollark: Let's say person A is connected to B, and A is also connected to the wider internet™ or something.
gollark: Your scheme would give me money for jamming someone else's connection so I can shove my own middle node in and just forward traffic to them.

See also

References
  1. Smith, Ryan (12 December 2016). "AMD Announces Radeon Instinct: GPU Accelerators for Deep Learning, Coming in 2017". Anandtech. Retrieved 12 December 2016.
  2. Shrout, Ryan (12 December 2016). "Radeon Instinct Machine Learning GPUs include Vega, Preview Performance". PC Per. Retrieved 12 December 2016.
  3. "Radeon Instinct MI6". Radeon Instinct. AMD. Retrieved 22 June 2017.
  4. "Radeon Instinct MI8". Radeon Instinct. AMD. Retrieved 22 June 2017.
  5. "Radeon Instinct MI25". Radeon Instinct. AMD. Retrieved 22 June 2017.
  6. Kampman, Jeff (12 December 2016). "AMD opens up machine learning with Radeon Instinct". TechReport. Retrieved 12 December 2016.
  7. Shrout, Ryan (12 December 2016). "Radeon Instinct Machine Learning GPUs include Vega, Preview Performance". PC Per. Retrieved 12 December 2016.
  8. Kampman, Jeff (12 December 2016). "AMD opens up machine learning with Radeon Instinct". TechReport. Retrieved 12 December 2016.
  9. "Radeon Instinct MI6". Radeon Instinct. AMD. Retrieved 22 June 2017.
  10. "AMD Radeon Instinct MI6 Specs". TechPowerUp.
  11. "Radeon Instinct MI8". Radeon Instinct. AMD. Retrieved 22 June 2017.
  12. "AMD Radeon Instinct MI8 Specs". TechPowerUp.
  13. Smith, Ryan (5 January 2017). "The AMD Vega Architecture Teaser: Higher IPC, Tiling, & More, coming in H1'2017". Anandtech.com. Retrieved 10 January 2017.
  14. "Radeon Instinct MI25". Radeon Instinct. AMD. Retrieved 22 June 2017.
  15. "AMD Radeon Instinct MI25 Specs". TechPowerUp.
  16. "AMD Radeon Instinct MI25 MxGPU Specs". TechPowerUp.
  17. "Next Horizon – David Wang Presentation" (PDF). AMD. AMD.
  18. "Radeon Instinct MI50". AMD. AMD.
  19. "Radeon Instinct MI50 Datasheet" (PDF). AMD. AMD.
  20. "Hands on with the AMD Radeon VII". PC Gamer. Jarred Walton.
  21. "Radeon Instinct MI60". AMD. AMD.
  22. "Radeon Instinct MI60 Datasheet" (PDF). AMD. AMD.
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