cooootce

3 ms·

1. 1.▲
   by cooootce 3y ago

   Thanks ! Yes, the main focus is on inference. It's possible to re-train a simple model at this power scale, but it's often time very small model and not deep-learning. Nanoedge AI studio from STelectronic give you some tool to tra
2. 2.▲
   by cooootce 3y ago

   In most MCU there is not an FPU so all floating point compute is emulated with software, so it's really slow. But yes, simple SIMD on integer improve so much the performance ! The main limitation is often not the time to process but th
3. 3.▲
   by cooootce 3y ago

   Great post with very interesting detail, thanks ! Another optimization could be to quantize the model, this transform all compute as int compute and not as floating point compute. You can lose some accuracy, but for any bigger model it&#x27
4. 4.▲
   by cooootce 3y ago

   Didn't know about it but their design decision is really cool (not very clear with the difference between the normal version and the "256 Mo" confusing). The software side doesn't seem very mature with very few help rega
5. 5.▲
   by cooootce 3y ago

   I had the opportunity to work on TinyML, it's a wonderful field! You can do a lot even with very small hardware. For example, it's possible to get real-time computer vision system with an esp32-s3 (dual-core XTensa LX7 @ 240 MHz