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1
Introduction
2
Why AI at the edge
3
Challenges
4
Complexity Accuracy
5
Jetson Platform
6
Jetson Computer
7
Jetson subsystems
8
Unified memory architecture
9
Software stack
10
Tensor RT
11
ML4 Benchmarks
12
Deep Stream SDK
13
Isaac SDK
14
Summary
15
Software
16
Ecosystem
17
Success
18
Future
19
Industrial
20
Developer Kits
21
CTSA Digital
22
Questions
23
How do you keep the overview
24
What circuits are available for Jetson Nano
25
Which type of smart city
26
New updates
27
Applications for Tiny Yellow
28
Availability of Jetson NX
29
What is differences between 10W and 5W operation
30
Energy consumption of Jetson
31
Google environmental sensor
32
Max power usage
33
Carrier boards
34
Recommendations
35
Roadmap
36
Hardware partners
37
Industrial protocols
38
Memory Upgrade
39
OpenCV Performance
40
Gigabit Ethernet Support
41
Tensor RT Compiler
42
Tensor IP
43
Exploration of different dimensions
44
Tensorflow
45
Jetson Nano
46
Synthetic Data
47
Conclusion
Description:
Explore NVIDIA's Jetson platform for deploying AI at the edge in robotics, video analytics, healthcare, industrial automation, and retail. Dive into the key hardware features of the Jetson family, the unified software stack enabling seamless development to deployment, and the ecosystem facilitating rapid time-to-market. Discover the latest product announcements, roadmap, and success stories from partners in this comprehensive 54-minute video. Gain insights into AI challenges at the edge, Jetson's computer subsystems, unified memory architecture, and software stack including TensorRT and DeepStream SDK. Learn about industrial applications, developer kits, and get answers to questions on topics such as Jetson Nano circuits, smart city implementations, and energy consumption. Understand the roadmap for future developments, hardware partnerships, and performance optimizations for various AI applications.

NVIDIA Jetson: Enabling AI-Powered Autonomous Machines at Scale

Nvidia
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