claude

ti-edgeai-skills

Train, compile, deploy and profile neural networks on Texas Instruments Edge AI processors (C7x/MMA with TIDL: TDA4VM, AM68A, AM69A, AM67A, AM62A). Five skills cover TIDL compilation and int8 calibration in Docker, host-vs-board output checks, edgeai-gst-apps configs and per-layer profiling. Asks before any board-state change.

3,741 on GitHubby saeedahmadicpv0.1.0MITupdated 2 days agodevelopment
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Plugin ti-edgeai-skills from marketplace buildwithclaude in davepoon/buildwithclaude. Commands read from the repo's manifest — yesterday.

What it installs

Skills (5)

  • ti-edgeai-dev

    Texas Instruments Edge AI SDK development for Jacinto 7 / AM6xA processors with a C7x DSP + MMA deep-learning accelerator (TDA4VM, AM68A, AM69A, AM67A, AM62A): TIDL model compilation with edgeai-tidl-tools, the on-board runtime (ONNX Runtime with the TIDL execution provider), edgeai-gst-apps configs

  • ti-edgeai-generate-config

    Build, validate and run edgeai-gst-apps YAML configs (inputs, models, outputs, flows) on TI Edge AI boards with a C7x/MMA accelerator (TDA4VM, AM68A, AM69A, AM67A, AM62A): video file, image sequence, USB webcam, CSI camera, RTSP or test-pattern input; one or several models; display with FPS overlay,

  • ti-edgeai-import-model

    Get a trained neural network that exports to a static-shape ONNX (a classifier, a segmentation model, or a detector with a TIDL meta-architecture such as YOLOX/SSD/YOLOv5-style) running on a TI Edge AI board's C7x/MMA accelerator (TDA4VM, AM68A, AM69A, AM67A, AM62A): preflight the ONNX, fold input n

  • ti-edgeai-profile-pipeline

    Measure and improve inference speed on TI Edge AI boards with a C7x/MMA accelerator (TDA4VM, AM68A, AM69A, AM67A, AM62A): model-only latency, per-layer C7x cycle hotspots, per-element GStreamer latency and FPS of the edgeai-gst-apps pipeline, system load, and the levers that trade accuracy for speed

  • ti-edgeai-train-model

    Plan and run training/fine-tuning so a vision model (detector, classifier, segmenter) deploys well on TI Edge AI processors with a C7x/MMA accelerator (TDA4VM, AM68A, AM69A, AM67A, AM62A): pick a TIDL-friendly architecture and input size, choose TI's toolchain (edgeai-modelmaker, edgeai-yolox, edgea

View source on GitHub buildwithclaude.com

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