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1
Introduction
2
About the company
3
Research Background
4
Neuromorphic Analog Signal Processing
5
Traditional Analog Signal Processing
6
Advantages of Analog Signal Processing
7
Analog AI Curves
8
Analog Computing
9
Memory Computing
10
Energy Cost
11
InMemory Computing
12
OnetoOne Mapping
13
Efficiency
14
Conclusion
15
Analog AI use case
16
Digital architecture
17
Audience questions
18
Sponsors
Description:
Explore a conference talk from tinyML Asia 2021 that delves into making signal chains more intelligent and efficient through mixed signal processing and in-memory computing. Learn about Reexen's innovative architecture that breaks down signal processing into mixed signal low-level feature extraction before digitization and mixed signal high-level in-memory computing after digitization. Discover how this approach offers significant improvements in energy consumption and cost efficiency compared to traditional signal chains. Gain insights into neuromorphic analog signal processing, advantages of analog signal processing, analog AI curves, and in-memory computing. Understand the challenges of traditional signal chains, including AD conversion bottlenecks and power consumption issues related to data transfer. Examine use cases for analog AI and digital architecture implementations. The talk concludes with audience questions and acknowledgment of sponsors.

Make the Signal Chain More Intelligent and Efficient with Mixed Signal Processing and In-Memory Computing

tinyML
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