Tech Notes

1. Tested flip pin speed of Arduino nano 33 BLE Sense Code:   while ( 1 ){     digitalWrite ( A0,HIGH ) ;     digitalWrite ( A0,LOW ) ; } Figure 1. approx. 426 kHz Why so slow: Control by mbed-OS  2. Tested SPI error at very low speed 34 became 68:  34: 0010 0010 68: 0100 0100 Problem: clock did not align . 3. Develop corrected SPI and test its speed FAILED: With more than 75% error transmission, it will cost at least 4 times. Risk Evaluation: Continuing SPI development by directly setting registers will face two risks: It may take many days, even weeks, to learn and debug the low-level driver for Arduino-nRF register development due to the lack of documents. There is still a risk of the not aligned clock problem. (might make many efforts in vain) 4. Direct operate register NRF_P0 -> DIRSET = 0x FF ;  //P0 lower 8bit = Input Mode while ( 1 ){   NRF_P0 -> OUTSET = 0x FF ;  // P0 lower 8bit = Output ...

Frameworks @PC PyTorch TensorFlow MXNet JAX Frameworks @mobile phones TVM TF-Lite MNN MCNN TensorRT (NVIDIA) Frameworks @MCU CMSIS-NN (ARM) X-Cube-AI (STMicroelectronics) TF-Lite Micro (TensorFlow) microTVM (apache) MCUNet (MIT Han’s lab) TinyNAS TinyEngine Original Paper:  https://ieeexplore.ieee.org/document/10284551

 Embedded Machine Learning (ML)  advantages: 1. low cost 2. more use cases 3. less delay 4. energy efficient 5. privacy Edge Computing (EC) challenges [1]:  1. task scheduling 2. resource allocation 3. delay optimization 4. energy consumption optimization 5. privacy and security [1] Hua, H., Li, Y., Wang, T., Dong, N., Li, W. and Cao, J., 2023. Edge computing with artificial intelligence: A machine learning perspective.  ACM Computing Surveys ,  55 (9), pp.1-35.

When it comes to developing and deploying in the field of TinyML, there are several tools, libraries, and frameworks available to assist developers in creating, training, and deploying small-scale machine learning models on resource-constrained devices. These tools and libraries enable inference on embedded devices while also allowing for model quantization, pruning, optimization, and other operations to adapt to resource-limited environments. Here are some examples: Tools and Frameworks: TensorFlow Lite for Microcontrollers (TFLite Micro) : TensorFlow Lite, introduced by Google, is a tool for running TensorFlow models on mobile and embedded systems. TFLite Micro is a version tailored for microcontrollers and similar small devices, supporting deployment of lightweight models. Edge Impulse : Edge Impulse is a comprehensive platform for developing, deploying, and managing machine learning models on embedded devices. It offers a graphical interface for streamlined data collection, model t...

Refers: https://lehre-svn.emsp.tu-berlin.de/Evicab/asignal/10sornmo/video/index.htm What is bioelectricity measured?  Inside the cell - cellular Inside the body - invasive On the body surface - noninvasive Purpose of biomedical signal processing To improve signal quality. To improve measurement accuracy. To reduce the amount of data. To visualize complex events. To make therapeutic devices, such as pacemakersand hearing aids, more intelligent.