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Overview

Bitweaver is a platform by DeepGate that compiles PyTorch models to optimized C libraries for microcontrollers and profiles them on real hardware.

You give it a model. You get back a deployable library and measured numbers from the exact MCU you're targeting: inference time, a per-layer breakdown of inference time, memory footprint, and flash.

Who it's for

Engineers and teams shipping AI to microcontrollers who need:

  • A path from PyTorch to an highly optimized, MCU-ready C library without hand-porting layers.
  • Real, measured numbers from the target hardware - not host-side estimates.
  • A shared place to compare model variants across boards and iterations.

What it isn't

  • Not a training platform: Bring a trained model, or a pre-trained / fine-tuned model from the Model Zoo.
  • Not a runtime: Bitweaver produces a static library you link into your app.
  • Hardware scope: See Supported Hardware for the current list of tested boards and MCU architectures.

Workflow

Bitweaver workflow

The workflow is iterative:

  • If the model misses your defined requirements (e.g. accuracy), stay in the developer environment and retrain or fine-tune.
  • If the model's on-MCU performance (inference time, memory, flash) misses your requirements, go back and redesign.

Bitweaver speeds up this cycle by automating optimization, profiling, and deployment.

Benchmark lifecycle

Every upload creates a benchmark job that moves through these states:

Stage What's happening
build_queued Waiting for a free buildworker.
building Buildworker is optimizing and compiling the model.
run_queued Compile succeeded; waiting for target hardware to free up.
running Model is executing on the MCU and being profiled.
completed Finished successfully. Metrics and artifacts are available.
build_failed / run_failed Stopped with an error. See the Submission view for details.
cancelled You cancelled the job before it finished.

Managing jobs

Jobs are managed from the Run History panel on the project dashboard.

  • Cancel a job at any point before it reaches a terminal state.
  • Delete a single job, or an entire Submission (a group of jobs uploaded together).
  • Failures surface inline in the Submission view with the stage and error message.

Core concepts

Project - A named container for related benchmarks. One project, one dashboard, one shared set of default metrics and target boards. Use projects to compare variants of the same model, or to isolate work across teams.

  • Dashboard - Per-project view with a Performance Map (metrics across runs) and Run History (every job, its state, and its artifacts).

  • Project Settings - Owners can edit the name, description, visibility, default metrics, and default benchmark targets.

Model Zoo - Pre-built, validated models you can deploy or fine-tune without training from scratch. Each Zoo model exposes three actions:

  • Validate - generate a script that loads the model and writes its schema JSON.
  • Fine-tune - generate a script that adapts the model to your own dataset.
  • Deploy - download a compiled library and headers for your target board.

Schema JSON - The file the SDK writes alongside your model weights. It describes the model's I/O contract and layout so Bitweaver can compile and benchmark it. See Quick Start for where it lands.