Bringing AI to Billions of Devices
From
Effortlessly design, profile and deploy AI to microcontrollers.
Three steps from AI model to microcontroller
Embedded machine learning shouldn't be hard. Our drag-and-drop platform profiles your model across multiple MCUs and gets your AI running on device in minutes.
01Design & Train ML Model
Our ML models are optimised for microcontroller (MCU) deployment from the design phase — not retrofitted at a later stage.
02Upload & Profile
Upload your model to our platform. Profile it across multiple MCUs simultaneously, across vendors.
03Download & Deploy
Download your compiled model files, include them in your application, and you’re running.
Built for maximum efficiency
NASA landed on the moon with 4 KB of RAM. We bring that same efficiency to AI at the edge.
1. DeepGate compiler
Purpose-built for performance - up to
vs Google’s TensorFlow Lite for microcontrollers on MLPerf Tiny models.
Cortex-M7 @ 280 MHz. Not verified by MLCommons.
2. DeepGate compression
Do more with less - up to
Automated agentic compression that retains model accuracy on MLPerf Tiny models.
Cortex-M7 @ 280 MHz.
3. DeepGate SDK
Custom layers built for efficiency - up to
Leverage our R&D advances — starting with our logic neural networks.
Cortex-M7 @ 280 MHz. Vs TFLM on anomaly detection
Built for teams shipping AI at the edge
Product & engineering teams
Your models, your data, your IP. Ship efficient on-device AI in your products.
Turnkey ML solutions
From idea to solution, we handle everything: model design, optimization, and deployment.
Silicon & platform partners
Offer your customers high performance AI through your platform without building it yourself.
The team behind DeepGate
We are scientists and engineers with PhDs, deep industry experience, and expertise in ML, compiler design, and embedded systems — on a mission to make every bit count.