Burgeoning machine learning and AI emerging use cases promise to create significant value for industries via accelerated information processing and increased accuracy of decision-making. But machine learning models are compute-intensive, demand high-frequency, and real-time AI analysis scenarios, which has led enterprises to lean on performance guidance using the metric Trillions of Operations Per Second (TOPS). TOPS captures “how many mathematical operations can an accelerator deliver in one second?” to compare and identify the best accelerator for a given inference task. By Rehan Hameed.
The article pays close attention to:
- Benchmarking with TOPS AI
- TOPS AI as a performance measure
- Higher TOPS does not equal higher performance
- Computational types
- Inference latency
For businesses making investments in Edge AI, calculating performance through benchmarking offers a reliable way to account for computational hardware structures versus TOPS. With most real-world applications requiring a blazing fast inference time, the best way to measure performance is to run a specific workload, typically ResNet-50, EfficientDet, a Transformer or a custom model to understand an accelerators efficiency. This is an older but still relevant article from 2022. Good read!
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