– Inference: Edge
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This benchmark suite measures how fast systems can process inputs and produce results using a trained model. Below is a short summary of the current benchmarks and metrics. Please see the MLPerf Inference benchmark paper for a detailed description of the motivation and guiding principles behind the benchmark suite.
Scenarios and Metrics
In order to enable representative testing of a wide variety of inference platforms and use cases, MLPerf has defined four different scenarios as described below. A given scenario is evaluated by a standard load generator generating inference requests in a particular pattern and measuring a specific metric.
|Scenario||Query Generation||Duration||Samples/query||Latency Constraint||Tail Latency||Performance Metric|
|Single stream||LoadGen sends next query as soon as SUT completes the previous query||1024 queries and 60 seconds||1||None||90%||90%-ile measured latency|
|Multiple stream||LoadGen sends a new query every latency constraint if the SUT has completed the prior query, otherwise the new query is dropped and is counted as one overtime query||270,336 queries and 60 seconds||Variable, see metric||Benchmark specific||99%||Maximum number of inferences per query supported|
|Server||LoadGen sends new queries to the SUT according to a Poisson distribution||270,336 queries and 60 seconds||1||Benchmark specific||99%||Maximum Poisson throughput parameter supported|
|Offline||LoadGen sends all queries to the SUT at start||1 query and 60 seconds||At least 24,576||None||N/A||Measured throughput|
Each benchmark is defined by a Dataset and Quality Target. The following table summarizes the benchmarks in this version of the suite (the rules remain the official source of truth):
|Area||Task||Model||Dataset||QSL Size||Quality||Multi-stream latency constraint|
|Vision||Image classification||Resnet50-v1.5||ImageNet (224x224)||1024||99% of FP32 (76.46%)||50 ms|
|Vision||Object detection (large)||SSD-ResNet34||COCO (1200x1200)||64||99% of FP32 (0.20 mAP)||66 ms|
|Vision||Object detection (small)||SSD-MobileNets-v1||COCO (300x300)||256||99% of FP32 (0.22 mAP)||50 ms|
|Vision||Medical image segmentation||3D UNET||BraTS 2019 (224x224x160)||16||99% of FP32 and 99.9% of FP32 (0.85300 mean DICE score)||N/A|
|Speech||Speech-to-text||RNNT||Librispeech dev-clean (samples < 15 seconds)||2513||99% of FP32 (1 - WER, where WER=7.452253714852645%)||N/A|
|Language||Language processing||BERT-large||SQuAD v1.1 (max_seq_len=384)||10833||99% of FP32 (f1_score=90.874%)||N/A|
Each Edge benchmark requires the following scenarios, and sometimes permits an optional scenario:
|Area||Task||Required Scenarios||Optional Scenarios|
|Vision||Image classification||Single Stream, Offline||N/A|
|Vision||Object detection (large)||Single Stream, Offline||N/A|
|Vision||Object detection (small)||Single Stream, Offline||N/A|
|Vision||Medical image segmentation||Single Stream, Offline||N/A|
|Speech||Speech-to-text||Single Stream, Offline||N/A|
|Language||Language processing||Single Stream, Offline||N/A|
MLPerf aims to encourage innovation in software as well as hardware by allowing submitters to reimplement the reference implementations. MLPerf has two Divisions that allow different levels of flexibility during reimplementation. The Closed division is intended to compare hardware platforms or software frameworks “apples-to-apples” and requires using the same model as the reference implementation. The Open division is intended to foster innovation and allows using a different model or retraining.
MLPerf divides benchmark results into Categories based on availability.
- Available systems contain only components that are available for purchase or for rent in the cloud.
- Preview systems must be submittable as Available in the next submission round.
- Research, Development, or Internal (RDI) contain experimental, in development, or internal-use hardware or software.
Each row in the results table is a set of results produced by a single submitter
using the same software stack and hardware platform. Each Closed division row contains the following information:
- Submitter: The organization that submitted the results.
- System: General system description.
- Processor and count: The type and number of CPUs used, if CPUs perform the majority of ML compute.
- Accelerator and count: The type and number of accelerators used, if accelerators perform the majority of ML compute.
- Software: The ML framework and primary ML hardware library used.
- Benchmark Results: Results for each benchmark as described above.
- Details: link to metadata for submission.
- Code: link to code for submission.
Each Open division row may add the following information:
- Model used: The model used to produce the results, which may or may not match the Closed Division requirement.
- Notes: arbitrary notes from submitter.
For results with power measurement, each row will add columns for each benchmark containing the following:
- System power (for Server and Offline scenarios), or
- Energy per stream (for Single stream and Multiple stream scenarios)
These metrics are computed using the measured average AC power (energy) consumed by the entire system for the duration of the performance measurements of a benchmark (e.g., a single network under a single scenario); the AC power is measured at the wall.
The measured power is only valid for the accompanying benchmark. MLPerf Power is only capable of measuring and validating the full system power. Any other references to power in any description (e.g., a TDP configuration, a power supply rating) are not measured or validated by MLCommons.
The rules are here.
The reference implementations for the benchmarks are here.