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1
Introduction
2
AI Power Smart Devices
3
Algorithms
4
Model Efficiency
5
Hardware Design
6
MPUS
7
Model Design
8
AIZip
9
Digital vs Analog
10
Digital AI Accelerator
11
Memory Access
12
Processing in Memory
13
Hardware CoDesign
14
ASF
15
Current Base Design
16
ChargeBased Design
17
CapRAM
18
Accelerator Architecture
19
PiM vs Digital
20
PiMnet
21
Why PiMnet
22
PiMnet vs ImageNet
23
Recap
24
Summary
25
Questions
26
Sponsors
Description:
Explore software-driven TinyML hardware co-design in this insightful conference talk by Weier Wan, Head of Software-Hardware Co-design at Aizip. Delve into the importance of software-hardware co-optimization for achieving greater efficiency in TinyML systems. Learn about Aizip's approach to accelerator co-design services for IC companies, leveraging production-quality TinyML models across various applications. Discover the potential of processing-in-memory (PIM) AI accelerators and how they significantly reduce power consumption. Gain insights into Aizip's full-stack PIM co-design services, including silicon-verified PIM IPs, chip architecture design, PIM-optimized neural networks, and PIM-aware training frameworks. Understand how this comprehensive co-design approach ensures superior system-level energy efficiency without compromising accuracy or robustness compared to digital processors.

Software-Driven TinyML Hardware Co-Design for AI Power Smart Devices

tinyML
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