How Engineers Are Using TinyML to Build Smarter Edge Devices

When designing a chip for mW class inferences, deep learning-based approaches in TinyML are showing promise in resolving network bandwidth bottlenecks. If a sensor on an MCU is taking in a large amount of raw data, deep learning techniques can reduce and refine the data into highly qualitative data. Such data requires far less network bandwidth. But reducing and refining the raw data into highly qualitative data that uses less bandwidth comes at a price, computationally speaking. Limiting the amount of energy that supports deep learning's computational complexity means testing every possible optimization configuration when designing a chip for mW class inference.