AzurEngine Accelerates MediaPipe Models On R8
In this project, the author demonstrates deploying Google MediaPipe palm-detection and hand-landmark models to AzurEngine's RPP-R8 (AE7100) accelerator, converting TFLite models to ONNX and optimizing them with AzurEngine SDK v1.6.11.7. They highlight the AE7100's specs — 1024 cores, 32 TOPS (INT8/INT32), 24MB SRAM, 16GB LPDDR4 at 59.7 GB/s, and typical 15W power — and note the flow avoids calibration data or GPU-based quantization, simplifying edge deployment and profiling for performance gains.
Key Points
- 1Converts TFLite MediaPipe models to ONNX and optimizes using AzurEngine SDK v1.6.11.7
- 2Eliminates calibration dataset needs by using RPP runtime, avoiding GPU-dependent quantization workflows
- 3Enables simpler edge deployment and potential low-power acceleration for embedded vision applications
Scoring Rationale
Detailed, actionable deployment with hands-on conversion and hardware specifics; limited novelty and single-source case-study scope.
Sources
Public references used for this report.
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