These Might Be the Fastest (and Most Efficient) AI Systems Around

IEEE Spectrum

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The machine learning industry’s efforts to measure itself using a standard yardstick has reached a milestone. Forgive the pun, but that’s actually what’s happened with the release of MLPerf Inference v1.0 today. Using a suite of benchmark neural networks measured under a standardized set of conditions, 1,994 AI systems battled it out to show how quickly their neural networks can process new data. Separately, MLPerf tested an energy efficiency benchmark, with some 850 entrants for that.

This contest was the first following a set of trial runs where the AI consortium MLPerf and its parent organization MLCommons worked out the best measurement criteria. But the big winner in this first official version was the same as it had been in those warm-up rounds—Nvidia.

Entries were combinations of software and systems that ranged in scale from Raspberry Pis to supercomputers. They were powered by processors and accelerator chips from AMD, Arm, Centaur Technology, Edgecortix, Intel, Nvidia, Qualcomm, and Xilininx. And entries came from 17 organizations including Alibaba, Centaur, Dell Fujitsu, Gigabyte, HPE, Inspur, Krai, Lenovo, Moblint, Neuchips, and Supermicro.

Despite that diversity most of the systems used Nvidia GPUs to accelerate their AI functions. There were some other AI accelerators on offer, notably Qualcomm’s AI 100 and Edgecortix’s DNA. But Edgecortix was the only one of the many, many AI accelerator startups to jump in. And Intel chose to show off how well its CPUs did instead of offering up something from its US $2-billion acquisition of AI hardware startup Habana.

Before we get into the details of whose what was how fast, you’re going to need some background on how these benchmarks work. MLPerf is nothing like the famously straightforward Top500 list of the supercomputing great and good, where a single value can tell you most of what you need to know. The consortium decided that the demands of machine learning is just too diverse to be boiled down to something like tera-operations per watt, a metric often cited in AI accelerator research.

First, systems were judged on six neural networks. Entrants did not have to compete on all six, however.

  • BERT, for Bi-directional Encoder Representation from Transformers, is a natural language processing AI contributed by Google. Given a question input, BERT predicts a suitable answer.
  • DLRM, for Deep Learning Recommendation Model is a recommender system that is trained to optimize click-through rates. It’s used to recommend items for online shopping and rank search results and social media content. Facebook was the major contributor of the DLRM code.
  • 3D U-Net is used in medical imaging systems to tell which 3D voxel in an MRI scan are parts of a tumor and which are healthy tissue. It’s trained on a dataset of brain tumors.
  • RNN-T, for Recurrent Neural Network Transducer, is a speech recognition model. Given a sequence of speech input, it predicts the corresponding text.
  • ResNet is the granddaddy of image classification algorithms. This round used ResNet-50 version 1.5.
  • SSD, for Single Shot Detector, spots multiple objects within an image. It’s the kind of thing a self-driving car would use to find important things like other cars. This was done using either MobileNet version 1 or ResNet-34 depending on the scale of the system.

Competitors were divided into systems meant to run in a datacenter and those designed for operation at the “edge”—in a store, embedded in a security camera, etc.

Datacenter entrants were tested under two conditions. The first was a situation, called “offline”, where all the data was available in a single database, so the system could just hoover it up as fast as it could handle. The second more closely simulated the real life of a datacenter server, where data arrives in bursts and the system has to be able to complete its work quickly and accurately enough to handle the next burst.

Edge entrants tackled the offline scenario as well. But they also had to handle a test where they are fed a single stream of data, say a single conversation for language processing, and a multistream situation like a self-driving car might have to deal with from its multiple cameras.

Got all that? No? Well, Nvidia summed it up in this handy slide:

And finally, the efficiency benchmarks were done by measuring the power draw at the wall plug and averaged over 10 minutes to smooth out the highs-and-lows caused by processors scaling their voltages and frequencies.

Here, then, are the tops for each category:

FASTEST

Datacenter (commercially available systems, ranked by server condition)

Image Classification Object Detection Medical Imaging Speech-to-Text Natural Language Processing Recommendation
Submitter Inspur DellEMC NVIDIA DellEMC DellEMC Inspur
System name NF5488A5 Dell EMC DSS 8440 (10x A100-PCIe-40GB) NVIDIA DGX-A100 (8x A100-SXM-80GB, TensorRT) Dell EMC DSS 8440 (10x A100-PCIe-40GB) Dell EMC DSS 8440 (10x A100-PCIe-40GB) NF5488A5
Processor AMD EPYC 7742 Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz AMD EPYC 7742 Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz AMD EPYC 7742
No. Processors 2 2 2 2 2 2
Accelerator NVIDIA A100-SXM-80GB NVIDIA A100-PCIe-40GB NVIDIA A100-SXM-80GB NVIDIA A100-PCIe-40GB NVIDIA A100-PCIe-40GB NVIDIA A100-SXM-80GB
No. Accelerators 8 10 8 10 10 8
Server queries/s 271,246 8,265 479.65 107,987 26,749 2,432,860
Offline samples/s 307,252 7,612 479.65 107,269 29,265 2,455,010

Edge (commercially available, ranked by single-stream latency)

Image Classification Object Detection (small) Object Detection (large) Medical Imaging Speech-to-Text Natural Language Processing
Submitter NVIDIA NVIDIA NVIDIA NVIDIA NVIDIA NVIDIA
System name NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT, Triton) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT, Triton) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT, Triton) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT)
Processor AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742
No. Processors 2 2 2 2 2 2
Accelerator NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB
No. Accelerators 1 1 1 1 1 1
Single stream latency (milliseconds) 0.431369 0.25581 1.686353 19.919082 22.585203 1.708807
Multiple stream (streams) 1344 1920 56
Offline samples/s 38011.6 50926.6 985.518 60.6073 14007.6 3601.96

The Most Efficient

Datacenter

Image Classification Object Detection Medical Imaging Speech-to-Text Natural Language Processing Recommendation
Submitter Qualcomm Qualcomm NVIDIA NVIDIA NVIDIA NVIDIA
System name Gigabyte R282-Z93 5x QAIC100 Gigabyte R282-Z93 5x QAIC100 Gigabyte G482-Z54 (8x A100-PCIe, MaxQ, TensorRT) NVIDIA DGX Station A100 (4x A100-SXM-80GB, MaxQ, TensorRT) NVIDIA DGX Station A100 (4x A100-SXM-80GB, MaxQ, TensorRT) NVIDIA DGX Station A100 (4x A100-SXM-80GB, MaxQ, TensorRT)
Processor AMD EPYC 7282 16-Core Processor AMD EPYC 7282 16-Core Processor AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742
No. Processors 2 2 2 1 1 1
Accelerator QUALCOMM Cloud AI 100 PCIe HHHL QUALCOMM Cloud AI 100 PCIe HHHL NVIDIA A100-PCIe-40GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB
No. Accelerators 5 5 8 4 4 4
Server queries/s 78,502 1557 372 43,389 10,203 890,334
System Power (Watts) 534 548 2261 1314 1302 1342
Queries/W 147.059077 2.842925634 0.1645961809 33.03209165 7.836384898 663.6193864

Edge (commercially available, ranked by single-stream latency)

Image Classification Object Detection (large) Object Detection (small) Medical Imaging Speech-to-Text Natural Language Processing
Submitter Qualcomm NVIDIA Qualcomm NVIDIA NVIDIA NVIDIA
System name AI Development Kit NVIDIA Jetson Xavier NX (MaxQ, TensorRT) AI Development Kit NVIDIA Jetson Xavier NX (MaxQ, TensorRT) NVIDIA Jetson Xavier NX (MaxQ, TensorRT) NVIDIA Jetson Xavier NX (MaxQ, TensorRT)
Processor Qualcomm Snapdragon 865 NVIDIA Carmel (ARMv8.2) Qualcomm Snapdragon 865 NVIDIA Carmel (ARMv8.2) NVIDIA Carmel (ARMv8.2) NVIDIA Carmel (ARMv8.2)
No. Processors 1 1 1 1 1 1
Accelerator QUALCOMM Cloud AI 100 DM.2e NVIDIA Xavier NX QUALCOMM Cloud AI 100 DM.2 NVIDIA Xavier NX NVIDIA Xavier NX NVIDIA Xavier NX
No. Accelerators 1 1 1 1 1 1
Single stream latency 0.85 1.67 30.44 819.08 372.37 57.54
System energy/stream (joules) 0.02 0.02 0.60 12.14 3.45 0.59

The continuing lack of entrants from AI hardware startups is glaring at this point, especially considering that many of them are members of MLCommons. When I’ve asked certain startups about it, they usually answer that the best measure of their hardware is how it runs their potential customers’ specific neural networks rather than how well they do on benchmarks.

That seems fair, of course, assuming these startups can get the attention of potential customers in the first place. It also assumes that customers actually know what they need.

“If you’ve never done AI, you don’t know what to expect; you don’t know what performance you want to hit; you don’t know what combinations you want with CPUs, GPUs, and accelerators,” says Armando Acosta, product manager for AI, high-performance computing, and data analytics at Dell Technologies. MLPerf, he says, “really gives customers a good baseline.”


Source: IEEE Spectrum

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