Inference Performance: APIs, Where Art Thou?

Having covered the new CPU complexes of both new Exynos and Snapdragon SoCs, up next is the new generation neural processing engines in each chip.

The Snapdragon 855 brings big performance improvements to the table thanks to a doubling of the HVX units inside the Hexagon 690 DSP. The HVX units in the last two generations of Snapdragon chips were the IP blocks who took the brunt of new integer neural network inferencing work, an area the IP is specifically adept at.

The new tensor accelerator inside of the Hexagon 690 was shown off by Qualcomm at the preview event back in January. Unfortunately one of the issues with the new block is that currently it’s only accessible through Qualcomm’s own SDK tools, and currently doesn’t offer acceleration for NNAPI workloads until later in the year with Android Q.

Looking at a compatibility matrix between what kind of different workloads are able to be accelerated by various hardware block in NNAPI reveals are quite sad state of things:

NNAPI SoC Block Usage Estimates
SoC \ Model Type INT8 FP16 FP32
Exynos 9820 GPU GPU GPU
Exynos 9810 GPU? GPU CPU
Snapdragon 855 DSP GPU GPU
Snapdragon 845 DSP GPU GPU
Kirin 980 GPU? NPU CPU

What stands out in particular is Samsung’s new Exynos 9820 chipset. Even though the SoC promises to come with an NPU that on paper is extremely powerful, the software side of things make it as if the block wouldn’t exist. Currently Samsung doesn’t publicly offer even a proprietary SDK for the new NPU, much less NNAPI drivers. I’ve been told that Samsung looks to address this later in the year, but how exactly the Galaxy S10 will profit from new functionality in the future is quite unclear.

For Qualcomm, as the HVX units are integer only, this means only quantised INT8 inference models are able to be accelerated by the block, with FP16 and FP32 acceleration falling back what should be GPU acceleration. It’s to be noted my matrix here could be wrong as we’re dealing with abstraction layers and depending on the model features required the drivers could run models on different IP blocks.

Finally, HiSilicon’s Kirin 980 currently only offers NNAPI acceleration for FP16 models for the NPU, with INT8 and FP32 models falling back to the CPU as the device are seemingly not using Arm’s NNAPI drivers for the Mali GPU, or at least not taking advantage of INT8 acceleration ine the same way Samsung's GPU drivers.

Before we even get to the benchmark figures, it’s clear that the results will be a mess with various SoCs performing quite differently depending on the workload.

For the benchmark, we’re using a brand-new version of Andrey Ignatov’s AI-Benchmark, namely the just released version 3.0. The new version tunes the models as well as introducing a new Pro-Mode that most interestingly now is able to measure sustained throughput inference performance. This latter point is important as we can have very different performance figures between one-shot inferences and back-to-back inferences. In the former case, software and DVFS can vastly overshadow the actual performance capability of the hardware as in many cases we’re dealing with timings in the 10’s or 100’s of milliseconds.

Going forward we’ll be taking advantage of the new benchmark’s flexibility and posting both instantaneous single inference times as well sequential throughput inference times; better showcasing and separating the impact of software and hardware capabilities.

There’s a lot of data here, so for the sake of brevity I’ll simply put up all the results up and we’ll go over the general analysis at the end:

AIBenchmark 3 - 1a - The Life - CPU (FP) AIBenchmark 3 - 1b - The Life - NNAPI (INT8) AIBenchmark 3 - 1c - The Life - NNAPI (FP16) AIBenchmark 3 - 2a - Zoo - NNAPI (INT8) AIBenchmark 3 - 2b - Zoo - CPU (FP) AIBenchmark 3 - 2c - Zoo - NNAPI (FP16) AIBenchmark 3 - 3a - Pioneers - CPU (INT) AIBenchmark 3 - 3b - Pioneers - NNAPI (INT8) AIBenchmark 3 - 3c - Pioneers - NNAPI (FP16) AIBenchmark 3 - 4 - Let's Play! - CPU (FP) AIBenchmark 3 - 5a - Masterpiece - NNAPI (INT8) AIBenchmark 3 - 5b - Masterpiece - NNAPI (FP16) AIBenchmark 3 - 6b - Cartoons - NNAPI (FP16) AIBenchmark 3 - 7a - Ms.Universe - CPU (INT) AIBenchmark 3 - 7b - Ms.Universe - CPU (FP) AIBenchmark 3 - 8 - Blur iT! - CPU (FP) AIBenchmark 3 - 9 - Berlin Driving - NNAPI (FP16) AIBenchmark 3 - 10a - WESPE-dn - NNAPI (FP16) AIBenchmark 3 - 10b - WESPE-dn - NNAPI (FP32)

As initially predicted, the results are extremely spread across all the SoCs.

The new tests also include workloads that are solely using TensorFlow libraries on the CPU, so the results not only showcase NNAPI accelerator offloading but can also serve as a CPU benchmark.

In the CPU-only tests, we see the Snapdragon 855 and Exynos 9820 being in the lead, however there’s a notable difference between the two when it comes to their instantaneous vs sequential performance. The Snapdragon 855 is able to post significantly better single inference figures than the Exynos, although the latter catches up in longer duration workloads. Inherently this is a software characteristic difference between the two chips as although Samsung has improved scheduler responsiveness in the new chip, it still lags behind the Qualcomm variant.

In INT8 workloads there is no contest as Qualcomm is far ahead of the competition in NNAPI benchmarks simply due to the fact that they’re the only vendor being able to offload this to an actual accelerator. Samsung’s Exynos 9820 performance here actually has also drastically improved thanks to the new Mali G76’s new INT8 dot-product instructions. It’s odd that the same GPU in the Kirin 980 doesn’t show the same improvements, which could be due to not up-to-date Arm GPU NNAPI drives on the Mate 20.

The FP16 performance crown many times goes to the Kirin 980 NPU, but in some workloads it seems as if they fall back to the GPU, and in those cases Qualcomm’s GPU clearly has the lead.

Finally for FP32 workloads it’s again the Qualcomm GPU which takes an undisputed lead in performance.

Overall, machine inferencing performance today is an absolute mess. In all the chaos though Qualcomm seems to be the only SoC supplier that is able to deliver consistently good performance, and its software stack is clearly the best. Things will evolve over the coming months, and it will be interesting to see what Samsung will be able to achieve in regards to their custom SDK and NNAPI for the Exynos NPU, but much like Huawei’s Kirin NPU it’s all just marketing until we actually see the software deliver on the hardware capabilities, something which may take longer than the actual first year active lifespan of the new hardware.

SPEC2006: Almost Performance Parity at Worse Efficiency System Performance
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  • TechieSandy - Tuesday, April 2, 2019 - link

    The difference in the Dynamic Range between the Snapdragon and the Exynos variant may have less to do with processing than the triggering by the camera. In one of the pics the Exynos proxessor has better details in the shadows and more dynamic range.
    In this photo:

    It may be something triggering the HDR algorithm. It may have to do with it.
  • Andrei Frumusanu - Friday, April 5, 2019 - link

    That image you linked is the Snapdragon - check the difference in detail in the marble for example.
  • urbanman2004 - Tuesday, April 2, 2019 - link

    No wonder people are holding onto their phones at the most for 4 years as indicated by this article after seeing those prices. After selling my Galaxy S7, never again will I find myself being held captive from the ever tightening stranglehold made by Samsung
  • Kishoreshack - Tuesday, April 2, 2019 - link

    Excellent review by Andrei
    this is even better than what Brian Klug used to do
    your analysis is good & conclusion are spot on
    I think you missed to test the GPS on both phones
    which would make it a complete review
  • Andrei Frumusanu - Friday, April 5, 2019 - link

    GPS part taken note of, I'll see what can be done in the future.
  • nyonya - Wednesday, April 3, 2019 - link

    Will the test results be added to Bench soon? Also, any guesses as to the battery life of the (Snapdragon) S10e? There's finally a small flagship phone and I'll probably upgrade to it. And agreed on the power button location, it's stupidly high on all the S10 variants.
  • ElFenix - Wednesday, April 3, 2019 - link

    45° FoV corresponds with a 52 mm lens on a 35 mm camera, which most people would consider 'normal' rather than 'telephoto.' 77° FoV corresponds with a 27 mm lens on a 35 mm camera, which most people would consider 'wide angle.' 123° FoV corresponds with a 12 mm lens on a 35 mm camera, which most people would consider 'super wide angle.'

    so this phone doens't have a telephoto camera in the traditional sense. only normal, wide, and super-wide.
  • s.yu - Friday, April 5, 2019 - link

    Indeed it is and is what many have been complaining about, the terminology issue of phone cameras.
  • US3R13 - Sunday, April 7, 2019 - link

    I have a question in regard to the camera output of the Exynos model.
    Does the different we saw in the review only occur in the JPG or even if we shoot raw the difference still exists?
  • zoamster - Wednesday, April 10, 2019 - link

    This review was a real real treat.
    I have been waiting for this review ever since the devices launched and have checked the website more than 20 times. No review can be better than this and I hope the authors keep doing the excellent work.
    I wish samsung starts selling snapdragon Soc phones in India(which i know they won't) and stop making those shitty ass exynos processors and start including hifi dac as lg does. Maybe improve the camera software department as pixel's or apple's.
    It seriously feels bad that you spend a fortune to buy trash.
    One question- Will the unlocked SD variant work fine in India?
    PS- Thank you for make this excellent review and I hope to see more in future.

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