The IoT market is yet to see an “explosive growth" in TinyML. Is that due to inadequate hardware, ever-shifting software or not enough ML skills in the embedded community?
What’s at stake:
TinyML in embedded systems can be implemented many ways, often by leveraging beefed-up MCUs, DSPs, AI accelerators and Neural Processing Units (NPUs). The lingering dilemma is how best to develop embedded systems with machine learning that could fit in the budget of TinyML.
Almost every new technology overheats its industry’s imagination, followed by an announcement flood, promising new tools, software and hardware – all of which fuels dreams of rapid market growth and big volume sales.
Then, reality.
TinyML has reached this moment.