daop-illustration

DAOP Illustration

A working JavaScript illustration of the Dynamic AI Offloading Protocol (DAOP) using the WebNN API.

This project demonstrates how the proposed estimateQoS() API can work in practice: an application builds a weightless computation graph, estimates performance via operator micro-benchmarks and polynomial interpolation, decides whether to run locally or offload to the cloud, and — if local — downloads weights and executes inference.

Prerequisites

• A WebNN-capable browser (e.g., Chrome Canary with WebNN flags enabled). See installation guide.
• Node.js (for the static file server).

Running the Demo

npm install
npm start          # starts http://localhost:8080

Open http://localhost:8080/examples/background-blur/background-blur-demo.html in the WebNN-capable browser.

Background Blur Demo

The demo applies AI-powered background blur to a meeting photo using the MediaPipe Selfie Segmentation model. The workflow follows the DAOP protocol:

1. Build weightless graph — the model topology is recorded without downloading weights.
2. Estimate QoS — per-operator micro-benchmarks and interpolation produce a performance tier.
3. Offloading decision — if the tier is acceptable (< 10 s), run locally; otherwise offload to cloud.
4. Execute — download weights, compile the native WebNN graph, run inference, apply blur.

The right column of the demo exposes DAOP internals: operator benchmarks, estimation curves, computation graph visualization, and a timing comparison between estimated and actual latency.

Implementation Details

See IMPLEMENTATION.md for a detailed description of the estimation strategy, including shape-aware micro-benchmarks, polynomial interpolation, and the 7-tier performance classification.

Project Structure

src/                             # DAOP library (reusable)
  index.js                       # Public API
  polyfill.js                    # WebNN detection + DAOP initialization
  daop-context.js                # Wraps native MLContext with estimateQoS / bindConstants / compute
  daop-graph-builder.js          # IR graph builder (weightless constants)
  daop-graph.js                  # IR graph + Mermaid visualization
  ir/
    graph-ir.js                  # TensorDesc, IROperand, IRNode, IRGraph
    shape-inference.js           # Shape inference for conv2d, pool, resample, etc.
  qos/
    estimate-qos-interp.js       # Interpolation-based QoS estimation + tier assignment
    interpolation/
      poly-fit.js                # Polynomial fitting (Normal Equations, Gaussian)
      time-model.js              # TimeModelDatabase (stores points, fits, predicts)
    microbench/
      bench-runner.js            # Multi-size benchmark runner
      op-benchmarks.js           # Per-op benchmark configurations (xs–xxl)
examples/
  background-blur/               # Background blur demo (self-contained)
    background-blur-demo.html    # Interactive two-column demo page
    selfie-model.js              # Model graph definition + weight loader
    blur-renderer.js             # Image processing + blur compositing
    meeting.jpg                  # Sample input image

License

Apache 2.0