AMD Announces Radeon Instinct MI60 & MI50 Accelerators: Powered By 7nm Vega
As part of this morning's Next Horizon event, AMD formally announced the first two accelerator cards based on the company's previously revealed 7nm Vega GPU. Dubbed the Radeon Instinct MI60 and Radeon Instinct MI50, the two cards are aimed squarely at the enterprise accelerator market, with AMD looking to significantly improve their performance competitiveness in everything from HPC to machine learning.
Both cards are based on AMD's 7nm GPU, which although we've known about at a high level for some time now, we're only finally getting some more details on. GPU is based on a refined version of AMD's existing Vega architecture, essentially adding compute-focused features to the chip that are necessary for the accelerator market. Interestingly, in terms of functional blocks here, 7nm Vega is actually rather close to the existing 14nm "Vega 10" GPU: both feature 64 CUs and HBM2. The difference comes down to these extra accelerator features, and the die size itself.
With respect to accelerator features, 7nm Vega and the resulting MI60 & MI50 cards differentiates itself from the previous Vega 10-powered MI25 in a few key areas. 7nm Vega brings support for half-rate double precision – up from 1/16th rate – and AMD is supporting new low precision data types as well. These INT8 and INT4 instructions are especially useful for machine learning inferencing, where high precision isn't necessary, with AMD able to get up to 4x the perf of an FP16/INT16 data type when using the smallest INT4 data type. However it's not clear from AMD's presentation how flexible these new data types are – and with what instructions they can be used – which will be important for understanding the full capabilities of the new GPU. All told, AMD is claiming a peak throughput of 7.4 TFLOPS FP64, 14.7 TFLOPS FP32, and 118 TOPS for INT4.
Previously: AMD Returns to the Datacenter, Set to Launch "7nm" Radeon Instinct GPUs for Machine Learning in 2018
Related: AMD Previews Zen 2 Epyc CPUs with up to 64 Cores, New "Chiplet" Design
(Score: 2) by tibman on Thursday November 08 2018, @04:53PM (2 children)
Wish my job required cutting edge hardware so i could play with this stuff. Business application development blows. (okay, done venting, back to work!)
Anyone here need tools like this? For machine learning, compute, password breaking, or whatever?
SN won't survive on lurkers alone. Write comments.
(Score: 2) by takyon on Thursday November 08 2018, @06:04PM (1 child)
A GPU for machine learning? It's deepfakes [wikipedia.org] time!
[SIG] 10/28/2017: Soylent Upgrade v14 [soylentnews.org]
(Score: 0) by Anonymous Coward on Thursday November 08 2018, @06:21PM
Not unless they support CUDA.
It is annoying, but OpenCL does not cut it.