FPGA as the Accelerator of Choice in Data Centric applications

Shpe conference, Chandler, arizonaMario a. bolaňos – november 1ST 2019

An analogy …

CUSTOM LOGO

ASIC

Specífic to a company

High cost from the beginning

Requires high volume

Application Specific Integrated Circuit

DESIGNS PRE-FABRICATED

ASSP

FOR SALE

Designed for specific functions

They are for general purpose use and

anyone could use and purchase them

Application Specific Standard Product

WHITE BOARD

FPGA

Flexible y Configurable

It has pre-fabricated modules that

could be added to the “White board”

Field Programmable Gate Array

WHAT IS A FPGA?

Field Programmable Gate Array, Flexible, multi-functional reprogrammable silicon with

bare-metal speed and reliability and custom parallelism

Custom hardware functionality but most of its electronic functionality could be modified

During design phase

During assembly of producto at customer

It could be modified even after the product has been released to production

Benefits of FPGA Technology

4

• Performance

• Flexibility

• Time to market

• Cost

• Integration

• Reliability

• Energy Efficiency

• Acceleration of Computing

• Long-Term Maintenance

FIRST fpga INTRODUCED IN 1985… ¿WHY IS IT

BECOMING SO CRITICAL NOW?

SOME FACTS RELATED To datA Accumulated Data up to 2013 = 4.4ZB, data estimated at 2020 = 40 ZB

90% of all data in 2018 were generated within the last 2 years

5 million tweets per day

294 billion emails per day

4 Petabytes of data in Facebook per day

65 billion messages in Whatsapp per day

5 billion searches per day

95 million photos and videos in Instagram per day

The average internet user will generate

~1.5 GB of traffic per daySmart hospitals will be generating over

3 TB per daySelf driving cars will be generating over

4,000 GB per day… each

All numbers are approximatedhttp://www.cisco.com/c/en/us/solutions/service-provider/vni-network-traffic-forecast/infographic.htmlhttp://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/Cloud_Index_White_Paper.htmlhttps://datafloq.com/read/self-driving-cars-create-2-petabytes-data-annually/172http://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/Cloud_Index_White_Paper.htmlhttp://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/Cloud_Index_White_Paper.html

Self driving cars will be generating over

4 TB per day… eachA connected plane will be generating over

40 TB per dayA connected factory will be generating over

1 PB per day

radar

~10-100

KBper

second

sonar

~10-100

KBper

second

gps ~50 KBper

second

lidar

~10-70

MBper

second

~20-40

And More Facts related To DataBy 2020

¿whathappens in

anINTERNET minute?

The Solution to Data Computing: Smarter Compute Using High Performance Computing

10

Assuming 3 cycle per multiplication operation on a 3 GHz processor. A

single threaded processor can produce 1 new multiplication product

every 1 billionth of a second.

A 2012 ImageNet classification algorithm* takes a 256x256 pixel image

and classifies it against 1000 categories that the image might map to.

This (unoptimized) algorithm takes 12.2 trillion multiplies! With a single

multiply product every 1 billionth of a second, it would take 12,200

seconds to categorize (3 hours, 24 minutes) at one multiply every one

billionth of a second.

https://vast.cs.ucla.edu/sites/default/files/publications/CNN_ICANN14.pdf

High-performance computing (HPC) is the use of parallel processing for running advanced

application programs efficiently, reliably and quickly. The term applies especially to systems that

function above a teraflop or 1012 floating-point operations per second.

THE Balancing data computing Act

11

CPU GPU FPGA ASIC & ASSP Heterogeneous

Peak

Performance

Moderate High Very High Highest Very High

Power

Consumption

High Very High Very Low Lowest Moderate

Flexibility Highest Moderate Very High Lowest Very High

Cost Moderate High Very High Highest* Very High*

Parallelism Very Low Very High Custom Custom Custom

In reality, one architecture cannot solve all the world’s compute problems.

Real-World Applications

How to Choose THE BEST DATA COMPUTING

PROCESSOR?

13

FRONT-END

• What are you trying to achieve?

• Which specifications (speed,

power, cost, time to market) are

most important?

• How many units will you need?

• Where will it be deployed?

• How often to you expect your

design to change?

• What is the expertise of your

engineering team?

• Has someone already built a

solution that’s “good enough”?

CPU GPU ASSP

ASIC FPGA

14

FPGA FOCUS MARKETS

Cloud Computing Autonomous DrivingSmart Cities

Networking5G Wireless Aerospace

Transforming Data Centers and cloud computing

15

CPU GPU ASSP

ASIC FPGA

Artificial Intelligence

Big Data Analytics (Hadoop, SPARK, SQL, NoSQL)

Video Transcoding

Network functions virtualization

Storage Acceleration

Security and DPI (Deep Packet Inspection)

16

CASE STUDY: Microsoft

125%Gain in

Throughput

29%DECREASE in Latency

8XIncrease in speed

With 15% less power

Accelerating the Critical tasks of autonomous

driving

17

Sensor Fusion

AI/Machine Learning

Functional Safety

5g connectivity

18

Case Study: Embedded Systems

Video: How Intel FPGAs Enable the Industrial Internet of Things

19

Case Study: Video and Vision

Real-Time Analytics

Simultaneous motion

detection, facial

recognition, and object

detection

Multiple input feeds

Flexible Sensor Interfaces

Adapt to changes in

proprietary interfaces

without changing the

rest of the design

Signal Processing

Pick and choose video

processing functionality

using IP cores

Accelerate pre-processing

of high-res videos

Video Compression

Integrate CODECs with

other processing functions

on a single FPGA

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What is ARTIFICIAL INTELLIGENCE (AI)?Artificial Intelligence

Data Analytics

Build a representation, query, or model that enables descriptive,

interactive, or predictive analysis over any amount of diverse data

Sense, learn, reason, act, and adapt to the real world without

explicit programming

Perceptual Understanding Detect patterns in audio or visual data

Machine LearningComputational methods that use learning algorithms to build a model from data (in supervised,

unsupervised, semi-supervised, or reinforcement mode)

Deep LearningAlgorithms inspired by neural networks with multiple

layers of neurons that learn successively complex

representations

Convolutional Neural Networks

(CNN)DL topology particularly effective at

image classification

AI is Transforming Industries

21

Smart

Assistants

Chatbots

Search

Personalization

Augmented

Reality

Robots

Enhanced

Diagnostics

Drug

Discovery

Patient Care

Research

Sensory Aids

Algorithmic

Trading

Fraud

Detection

Research

Personal

Finance

Risk Mitigation

Support

Experience

Marketing

Merchandising

Loyalty

Supply Chain

Security

Defense

Data Insights

Safety &

Security

Resident

Engagement

Smarter Cities

Smart Grid

Conservation

Operational

Improvement

Oil & Gas

Exploration

Automated Cars

Automated

Trucking

Aerospace

Shipping

Field

Automation

Factory

Automation

Predictive

maintenance

Precision

Agriculture

Field

Automation

CONSUME

R

HEalth FINANCE RETAIL GOVERNME

NT

ENERGY TRANSPOR

T

INDUSTRIA

L

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BENEFITS OF Intel FPGAS fOR AI

Power Efficiency

Reduced total cost of ownership

Speed

Real-time decision making

Throughput

Do more with less

Deployment Flexibility

Offloaded or in-line processing

I/O Types

Direct interface to data source

Power envelope

Only use as much as you need

Precision

Customizable precision and data types

Future Algorithms

Adaptable to architectures of the future

Multi-functionality

AI and more, all on one chip

PERFORMANCE HARDWARE FLEXIBILITY WORKLOAD FLEXIBILITY

Management,

Sensors and

edge devices

Vision systems,

and purpose-built,

application-specific hardware

Scalable and efficient

computing performance

Cloud, datacenter,

and HPC

Copyright © 2017, Intel Corporation. All rights reserved.

Board Management

Edge Compute

I/O Expansion

Automobile sensors, traffic sensors

Machine Vision

Embedded Vision

Robotics

Infotainment

Datacenter

Networking

Military / Defense

ADAS

Datacenter / CSP Acceleration

5G Wireless Infrastructure

Network Communications

Military / Defense

The right performance and features for the right application

Intel® FPGA : application specific performance

High-Level Design (HLD) Portfolio

24

Algorithm

Designer

Software

Programmer

Embedded

Designer

Hardware

Designer

Intel® FPGA SDK

for OpenCL™

DSP Builder for

Intel® FPGAs

Intel® HLS

Compiler

HDL Code,

Qsys (schematic)

PE

RS

ON

A

FRONT-END

Generated RTL

Generated RTL

Quartus®

PrimeGenerated RTL

Generated RTL

READY TO LEARN MORE?

25

Online Training

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FPGA training courses.

Intel FPGA YouTube

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technology

Design Examples

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with ready-to-use design examples

Community Forum

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software and hardware.

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Videos and classes on AI, Machine

Learning and Deep Learning

THANKS