Maximum AI. Minimal hardware.

DeepGate enables realtime Edge AI on microcontrollers using lightweight, logic-based machine-learning models that fit within strict memory and power limits — unlocking AI capabilities where nothing else can.

Taking AI to the bit level.

Technology

DeepGate provides Logic Neural Networks (LNNs) instead of costly Artificial Neural Network (ANNs) — unlocking sophisticated, fast, and tiny AI.

Developer Experience

Our precompiled models ship as two standalone files that integrate into existing workflows with minimal code changes.

Business Impact

Enable new markets, cut hardware costs, and reduce key individual risk - maximizing ROI while minimizing expenses.

Technology

Problem with Edge AI landscape

Most AI is too heavy for the edge today, creating a fundamental barrier for millions of products. Artificial Neural Networks (ANNs) power almost all modern AI - from voice to image recognition. However, these models are designed for powerful chips like GPUs, not for tiny microcontrollers (MCUs) that power everyday products - from appliances and wearables to sensors and industrial devices. When you try to run ANNs on MCUs, the shortcomings become evident: there is not enough memory to fit the model, not enough compute to run it fast, and the battery drains quickly. Even small AI tasks become slow, inconsistent, or even impossible.

Our technological solution

DeepGate overcomes the fundamental Edge AI barrier by moving beyond the heavy, power‑hungry core of today’s neural networks—matrix math—to an entirely new approach: logic. Matrix math is the backbone of ANNs, but it requires expensive hardware accelerators and does not run efficiently on tiny MCUs. Instead, our proprietary models use fast, lightweight logic operations—working directly on bits rather than large floating‑point numbers. By reducing computation to bit‑level logic, we unlock dramatically lower memory use, lower power draw, and far faster execution on even the smallest MCUs. This is not just another compression trick or quantization tweak; it is a fundamentally different technology built from first principles and engineered for real‑world deployment. DeepGate is transforming this technology into a new industry standard, empowering companies to unlock powerful, reliable AI on hardware previously out of reach.

Do you have questions?

Selected case studies

The results speak for themselves

Our selected case-studies demonstrate exceptional performance across critical metrics, consistently outperforming industry standards in accuracy, speed, and efficiency. These case-studies were performed on a Arm Cortex-M7 MCU and represent milestones in bringing sophisticated AI to resource-constrained environments, proving that edge devices can deliver enterprise-grade intelligence without compromise.

Anomaly Detection

Real-time identification of unusual patterns and outliers in data streams

Face Recognition

Fast, accurate facial authentication with privacy-focused local processing

Inference speed (ms)

RAM (KB)

FLASH (KB)

For detailed information about our benchmarking methodology, please get in touch with us.

Built for Developers

Getting ANN models onto MCUs is a painful process. Developers fight with vendor-specific toolchains, bugs and constant tuning just to make it work. With DeepGate, getting started is as simple as copy-pasting two files. The .h file serves as your full API reference, which includes everything you need to start using the model. The .lib file is your static model library.

Want to start developing? Let us know!