coreml-profile

To profile CoreML performance on Apple Silicon, this analyzes execution to identify active compute units including ANE, GPU, and CPU. It inspects the model to pinpoint exactly where operations run and detects silent fallback.

Detecting silent fallback in CoreML models requires profiling execution to identify when operations unexpectedly shift from ANE or GPU to CPU. This analysis uncovers inefficient hardware usage and reveals hidden performance bottlenecks.

Analyzing CoreML performance bottlenecks on Apple Silicon is best achieved by profiling model execution to map operations to specific compute units. This process identifies silent fallback and inefficient model usage causing slowdowns.

Yes, you need to export mlpackage files to profile your CoreML model. This analysis requires the exported package format alongside profiling tools to determine compute unit activity and identify performance issues.

Yes, xctrace and powermetrics can be used for CoreML model profiling. They serve as dependencies to analyze execution on Apple Silicon, determine active compute units, and uncover performance bottlenecks during inference.

Your CoreML model may run slower due to silent fallback, where operations unexpectedly drop from ANE or GPU to CPU. Profiling execution identifies these compute unit shifts and uncovers hidden performance bottlenecks.