microsoft/EdgeML

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1681 stars · 387 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A library from Microsoft Research India implementing resource-efficient machine learning algorithms (Bonsai, ProtoNN, FastGRNN, EMI-RNN, DROCC, RNNPool) for edge and IoT devices. It provides TensorFlow and PyTorch packages, C++ inference code, and a fixed-point quantization tool (SeeDot) so tiny models can run offline on microcontrollers.

Use cases

• train tiny classifiers that fit in kilobytes on IoT sensors
• run gesture recognition on microcontrollers
• efficient RNN inference on RAM-constrained devices
• anomaly detection for resource-scarce edge devices
• quantize models to fixed-point arithmetic for embedded inference
• recover critical signatures from time series for fast RNN predictions

When to choose

• you need ML models with kilobyte-scale footprints for IoT or embedded hardware
• you want offline, low-latency predictions without cloud connectivity
• you're working with time-series sensor data on constrained devices

When to avoid

• you need state-of-the-art accuracy on large-scale datasets with ample compute
• you want a general-purpose deep learning framework rather than specialized edge algorithms
• you need actively developed features or broad community support

Facets

library · maturity maintenance

machine-learning deep-learning llm-training machine-learning iot embedded-systems python cpp cross-platform embedded edge-ml tinyml rnn bonsai protonn fastgrnn quantization tensorflow pytorch microsoft-research

Member repositories

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markdown · JSON · MCP: product_card(name="microsoft/EdgeML")