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1
Introduction
2
Presentation
3
Optimizations
4
Cycle Measurements
5
Cycle Bound Analysis
6
Memory Bound Analysis
7
Library Optimizations
8
Fully Connected Operators
9
Loop unrolling
10
Checking compiler output
11
Cycle bound improvement
12
Simplifications
13
Summary
14
Hyperparameter Optimization
15
Question for Felix
16
TensorFlow Lite for microcontroller
17
Merging CMSISNN and optimized kernels
18
Reference kernels
19
Optimization
20
Person Detection
21
Hardware
22
Demo
23
Questions
Description:
Explore performance optimizations for Edge AI applications in this tinyML Talks local webcast featuring Felix Johnny and Fredrik Knutsson from Arm. Dive into identifying bottlenecks in ML model inference and learn effective solutions, focusing on the CMSIS-NN library for Arm Cortex-M processors. Discover common optimization methodologies, understand how operator shapes affect performance, and gain insights for model designers. Witness a live demonstration of CMSIS-NN with TensorFlow Lite for Microcontrollers on an Arduino Nano 33 BLE sense board, showcasing the benefits of optimization techniques. Cover topics such as cycle measurements, memory bound analysis, library optimizations, hyperparameter optimization, and person detection on embedded hardware.

CMSIS-NN & Optimizations for Edge AI

tinyML
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