A Look At AI Benchmarking For Mobile Devices In a Rapidly Evolving Ecosystem

MojoKid writes: AI and Machine Learning performance benchmarks have been well explored in the data center, but are fairly new and unestablished for edge devices like smartphones. While AI implementations on phones are typically limited to inferencing tasks like speech-to-text transcription and camera image optimization, there are real-world neural network models employed on mobile devices and accelerated by their dedicated processing engines. A deep dive look at HotHardware of three popular AI benchmarking apps for Android shows that not all platforms are created equal, but also that performance results can vary wildly, depending on the app used for benchmarking.
Generally speaking, it all hinges on what neural networks (NNs) the benchmarks are testing and what precision is being tested and weighted. Most mobile apps that currently employ some level of AI make use of INT8 (quantized). While INT8 offers less precision than FP16 (Floating Point), it’s also more power-efficient and offers enough precision for most consumer applications. Typically, Qualcomm Snapdragon 865 powered devices offer the best INT8 performance, while Huawei’s Kirin 990 in the P40 Pro 5G offers superior FP16 performance. Since INT8 precision for NN processing is more common in today’s mobile apps, it could be said that Qualcomm has the upper hand, but the landscape in this area is ever-evolving to be sure.

Read more of this story at Slashdot.



Source: Slashdot – A Look At AI Benchmarking For Mobile Devices In a Rapidly Evolving Ecosystem

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