TinyML Feasibility Study (MCU Edge AI)
$0.00
Find out—before you build—whether TinyML will actually work on your MCU in terms of memory, latency, accuracy, and deployment effort.
Description
This feasibility study is for startups, product teams, and R&D groups exploring on-device AI on microcontrollers and wanting a clear yes/no answer before committing engineering time and budget.
What You Get
✔ TinyML feasibility report
Clear assessment of RAM, Flash, compute, and latency budgets based on your target MCU and use case.
✔ Model approach recommendation
Guidance on quantization, pruning, or architecture changes needed to fit and run efficiently.
✔ Data strategy guidance
Actionable plan for data collection, labeling, and dataset sizing aligned with TinyML constraints.
✔ Deployment sketch
High-level deployment path using TensorFlow Lite Micro (TFLM) or a suitable alternative.
✔ Risk log & roadmap
Identified technical risks with recommended next steps toward a PoC or production implementation.
Scope (What’s Included)
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MCU-based inference feasibility (no cloud dependency)
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Single use-case / inference task
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Analysis focused on fit, performance, and practicality
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Architecture-level recommendations (not full model training)
Full model training, firmware implementation, or hardware redesign are handled as follow-up services.
Feasibility report delivered with clear constraints and conclusions
Recommended TinyML approach documented
Deployment path outlined
Risks explicitly identified with mitigation options
Clear “go / adjust / don’t proceed” guidance
