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Published
17 April 2026
Written by Yunus Unal Mechatronics Engineer and Content Specialist
Yunus is a mechatronics engineer with a background in 5G mobile communications and intelligent embedded systems. Before joining TKO and ipXchange, he developed and tested IoT and control-system prototypes that combined hardware design with embedded software. At ipXchange, Yunus applies his engineering knowledge and creative approach to produce technical content and product evaluations.
At Embedded World 2026, DeepGate presented a platform aimed at one of the most persistent challenges in edge AI: understanding how a trained model will actually perform on embedded hardware before committing to integration.
The platform focuses on a simple workflow. Engineers start with a trained model, typically developed in PyTorch, and upload it to DeepGate’s web-based environment. From there, the system compiles and benchmarks the model across multiple target devices, providing visibility into flash usage, RAM consumption, and inference time.
For engineers evaluating AI deployment on microcontrollers, this removes a common bottleneck. Instead of manually porting models and discovering limitations late in the process, performance can be assessed within minutes on real hardware. The platform also provides layer-level breakdowns, allowing engineers to identify bottlenecks and iterate directly in the model before deployment.
A key design decision is that DeepGate uses its own runtime layers rather than retrofitting existing ones. This ensures that the behaviour and accuracy of the trained model remain consistent when deployed on the target microcontroller, avoiding the typical mismatch between development and embedded execution.
Deployment is intentionally lightweight. Once performance targets are met, the platform outputs a header file and static library that can be dropped into an embedded project. This keeps integration close to standard embedded workflows and reduces the effort required to move from evaluation to implementation.
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