The document expresses gratitude to global and local sponsors for an event in 2018, providing a brief overview of advancements and requirements in cloud services and AI technology. It highlights the significant increase in data volume from 2013 to 2020 and discusses the capabilities and applications of FPGAs in AI training and inference. Additionally, it includes links to various resources and social media profiles.

2. Nossos agradecimentos aos patrocinadores globais de 2018!

3. E um GRANDE agradecimento ao nosso patrocinador local!
http://www.venturus.org.br

4. Microsoft MVP Azure
vitormeriat.com.br
linkedin.com/in/vitormeriat
twitter.com/vitormeriat
github.com/vitormeriat
youtube.com/vitormeriat

5. Agenda

6. A faster, more efficient, more intelligent cloud
Data explosion: 2013 4.4 ZB - 2020 44 ZB
ML, DNN, AI are driving requirements up faster
Autonomous decision making
Real-time insights into connected devices
Interactive user experiences
Cloud-scale services
Searches and recommendations (Indexing the Internet!)
The need for SCALE
The need for LOW-LATENCY
The need for THROUGHPUT
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2013
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2020
4.4 ZB 44 ZB
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0
1
0
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1
0
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0
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1
Source: IDC 2014

8. Soon Soon Soon Soon
https://aka.ms/gpupreviewndnc

9. Azure Batch AI Training
AI Training At
Scale with Azure

10. Autodesk 3ds Max / Maya
VM
Renderer
VM
Renderer
VM
Renderer
Integrated Client Plugin
Azure Batch
Monitoring
Reporting
Single bill

11. FPGAs
EVALUATION
CPUs and FPGAs,
ASICs under investigation
EFFICIENCY
TRAINING
CPUs and GPUs, limited FPGAs,
ASICs under investigation
Control
Unit
(CU)
Registers
Arithmetic
Logic Unit
(ALU)
+
+
+
+
+
+
+
FLEXIBILITY
CPUs GPUs
ASICs

12. Microsoft has the world’s largest cloud investment in FPGAs
Multiple Exa-Ops of aggregate AI capacity
We have built powerful DNN serving platform on our FPGA fabric
FPGAs ideal for adapting to rapidly evolving ML
CNNs, LSTMs, MLPs, reinforcement learning, feature extraction, decision trees, etc.
Inference-optimized numerical precision
Custom binarized, ternarized, tiny precision nets
Sparsity, deep compression for larger, faster models
Tens to hundreds of TOPS of effective inference throughput at low batch sizes
Ultra-low latency serving on modern DNNs
>10X better than CPUs and GPUs
Scale to many FPGAs in single DNN service
Performance
Flexibility
Scale

14. Eu quero ensiar para o meu computador
que isso é um gato
O que é
um gato?
Eu sou
um gato

15. Vamos ensinar esse computar

16. Isso é um gato.
Agora eu já sei.

17. WTF?!?!

18. WTF?!?!

24. [8]

25. [1,2,3]

26. [1,2,3]
[4,5,6]
[7,8,9]

28. Vetor Matriz Tensor

29. Perceptron

30. What?

31. Generative Adversarial Networks

35. vitormeriat.com.br
linkedin.com/in/vitormeriat
twitter.com/vitormeriat
github.com/vitormeriat
youtube.com/vitormeriat
Thank you!