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1
Introduction
2
About tinyML
3
Multilingual speech recognition
4
Automatic Intent Recognition
5
Demo
6
Sam
7
System Overview
8
Common Challenges
9
Model Compression
10
DTPG
11
Wakeboard
12
Microcontrol library
13
Streaming
14
Streaming example
15
Memory requirements
16
Streaming Microcore vs Tensorflow Lite
17
Running larger models on smaller devices
18
Live demo
19
Smart home demo
20
Questions
21
Thanks
Description:
Explore speech recognition technology for low-power devices in this tinyML Talks webcast featuring Fluent.ai experts Vikrant Tomar and Sam Myer. Delve into the process of transitioning from high-level libraries like Pytorch to running models on ARM Cortex M series microcontrollers and DSPG digital signal processors. Discover optimization techniques including low-level programming, 8-bit quantization, unique model architectures, network compression, and layer selection. Learn about multilingual speech recognition, automatic intent recognition, and system challenges. Witness live demonstrations of smart home applications and compare streaming microcore performance with TensorFlow Lite. Gain insights into memory requirements and strategies for running larger models on smaller devices in this comprehensive exploration of speech recognition technology for resource-constrained environments.

Speech Recognition on Low Power Devices

tinyML
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