Presentation RuChip Pte Ltd

RuChip Pte Ltd SingaporeAnton GerasimovEvgeny KovalevVolkov Mikhail

Alexander BlinovKonstantine Stowolosow

© Ruchip Pte Ltd confidential 1

Ultra Low-powerSearching Microprocessor


1. Introduction: the problem, the solution, the customers.

2. Our solution: key technology, key challenges, POC stage objectives.

3. Business model. Go-to-market strategy

4. Funding and milestones

5. Summary6. Annex

Digital Universe growth


More information = more processing power:

Amount of Digital information in the world:• 2006 – 161 exabytes (exa - 1018)• 2008 – 450 exabytes• 2011 (forecast) – 1,800 exabytes!• The annual growth rate is

expected to be more than 60%

Every third server in the world is used for information search

In 2009 there was more than 35 million servers all over the world (US$60B)

Search servers market forecast

Every third server in the world is used for information searchToday the search servers market size is US$20B a year (source: IDC 2008, Gartner 2009)Web Search service evolution:

Demand for new servers: ~1.5M units a year, and growing


Text Text + Images

Text + Images + Video

2011 2016 2020

Real-time text search

Images recognition

and indexing

Video recognition and indexing

Servers units

Year

10M

20M

30M

40M

50M

The problem

Today’s x86 processors are not fast enough to index the information as it is being generated

Switching to low-power servers based on ARM/Atom CPU having recently appeared on the market (Seamicro, Calxeda) is not a solution


Google, Microsoft top executives: “We are not going for ARM/Atom based servers because the software portability overhead is too high”.

And what if the cost of a kWh in 5 years will be $1, not $0.11?

Energy factor: the necessity to cope with increasing demand for CPU cycles results in huge power consumption

So what is the solution?


New specialized processor withUltra-low power consumptionHigh performance due to the use of specialized functional blocks to alleviate the bottlenecks/overheads of a search engine:

Data serialization, RPCData compression/decompression, security Instant large index searching

Internal data formats optimized for those of a search engine.

Analogy:– an HDTV set-top box has a dozen of special processors and not an Intel x86 CPU. HDTV data formats are similar to what is used in search engines. It will be playing more and more important role as video indexing becomes a mainstream.

Who our customers will be?


Specifically: Search engines: Google, Yahoo,

new search start-ups

Social networks, social graphs

Hosting for Cloud providers

Future hosting (Amazon-like, e-commerce)

Year Staurtup SearchEngine Total VC Funding

2004Yahoo! Search

A9.com Amazon.com projectHakai $21 million

2005

MSN Search Microsoft projectAsk.com

GoodSearch Yahoo projectKosmix $55 million

Like.com $48 millionSearchMe $21 million

2006 Live Search Microsoft projectChaCha $58 million

2007

Wikiseek SearchMe projectWikia SearchBlackle.com Google project

Mahalo $58 millionPowerset $21 million

2008

ViewziCuil $33 million

BoogamiVADLO

2009 Bing Microsoft projectAverage $40 million

Search start-ups funding over the last yearsGenerally: Distributed <Key,Value> stores and

services based on them






5. Summary

6. Annex


Solution: a new architecture

The key functions of GPNP:Data processingPackets parsingPackets routing

The solution: a General Purpose Network Processor (GPNP) for search applications

The GP block is based on ARM/Atom cores and ensures low power consumption (x10 better efficiency)The NP block is responsible for alleviating Google’s bottlenecks and intercommunicating with other cores on the board


Solving highlighted problemsProblem Solution (RuChip)

The “Power Wall” (power consumption) ARM/Atom “mobile cores” (x10 better)Performance bottlenecks (Google) :1) Data serialization, compression, cryptography2) Instant large index searching

New transport system:1) GPNP architecture2) New protocol to support data structures3) Custom instructions extensions, HW acceleration.

Portability issue: software stack adaptation HW/SW adaptation layer

SoftwareStack

GPNP

LOAD BALANCER

GPNP

GPNP

GPNP

GPNP

GPNP

New protocol


RuChip Key Added Value

Goya micro-processor

cores

NoCFront-end

designBack-end

design

Chip Transport

System

ASIP design

The Transport System is the crucial link in the entire value chain


Design Targets

* Performance validation: Goya chip prototype vs. Amazon Elastic Compute Cloud** Power consumption estimation: CAD (Synopsys, Cadence)*** Workload is a Nutch/Hadoop framework, embedded version

Cost (ASP)

Power Consumption

Performance

Cost (ASP)

Power Consumption

Performance

Existing

RuChip


POC Objectives

Objective DescriptionDesign of the transport system (NP) –ARCH + RTL + System Model

Assess the power consumption and the best/worst performance

Integration between the GP and NP parts Work with ARM, Seamicro, Intel, Marvell

Protocol final design and validation Performance validation compared to Amazon Elastic Cloud Compute

Device drivers, firmware API for DMA, Decoders, Parsers. Parsers design for Google, Hadoop, etc.

Commercial software stack adaptation Software stack for different perspective applications (other than Google)

Work packages structure

Protocol parsing; Custom instructions; Encoders/decoders; Security; Transport channelsTransport system concept validation:

POC






5. Summary

6. Annex

RuChip Supply Chain


Data centersstate & corporate

New search engines,Start-ups

Search enginesglobal, regional,

specializedServer makers&

System integrators

Goya Software

Goya microprocessor

Goya specialized board

Boards/chipset makers

Social NetworksFacebook,MySpace

Search Engine

Goya = Google + Yahoo

Licensing


Estimated Market Size for the chip

When Real Time Indexing Market Size (Forecast) Application

2011 Text $1B Real time Web monitoring

2016 Text + Images $10B People search in the global Webbased on an image pattern

2020 Text + Images + Video $50B People search the Web for video pattern

1-st Goya chip generation: text indexing2-nd Goya chip generation: text and images indexing

Recognition & Indexing for social services:Buying power of Google (approx.): Year Cost of the new servers One server cost Processor cost New servers in Google / Year

2012 $2300,M $1250 $125 2,100,0002015 $3900,M $1250 $125 4,200,200

* Assumption: Google spends 33% of CAPEX to buy the new servers ** Assumption: Amortization period for the servers in Google is 4 years

There are similar technologies out there which can be compared to us:

Multicores (picoChip, Ambric, Tilera), Cisco Quantum, Octeonprocessor (Cavium).

Conventional x86 CPU makers (Intel, AMD) represent the biggest threat to our technology

Intel’s “Platform 2015” (RMS – Recognition, Mining, Synthesis for Tera-scale computing)

Nvidia, IBM, Sun (Oracle)Tesla, Cell etc.


Competitors


Disruptive innovation

Deep specialization and customization of the chip

Customization of the Transport System (NP) for the tasks and data structures of Google

Software:NOSQL FrameworksCommercial software stacks

(Cloud)

NP – optimization of the GP-GP communicationGP – general purpose part (can be either ARM or Intel)

Main algorithms: Large batch processing (MapReduce)Real time indexing (Dremel)

GPNP

LOAD BALANCER

GPNP

GPNP

GPNP

GPNP

GPNP

Go-To-Market Strategy


Product1-st stage: Transport IP (NP): ASIC + boards2-nd stage: Multicore chip (GPNP) + boards

Marketing channelsIntegrators (Novell,..) Direct marketing

Target marketsB2B: Search engines, Hosting for NOSQL, Cloud providers, Webmail hosting, Social networksB2C: Mini-search engines (real-time)100+ Software applications

CASE:






5. Summary

6. Annex


Problem

Proposed Solution

Business Model

• Problem: Insufficient performance and poor energy efficiency of large search engines; The situation will deteriorate in several years as the workload increases exponentially (video content indexing)Example: Google’s annual power consumption cost is about $1B. Cost of the new servers is more $1B /Y

• Potential customers are: Search engines, Hosting for NoSQL, Webmail hosting, Social networks. Examples: Google, Yahoo, Blekko, Facebook, Yandex, Mail.ru, Microsoft, Baidu, Panguso.com

• The solution: ARM/Atom - based servers and optimization of the distributed communication system• Key challenges: Optimization of the transport processing system (custom instructions, HW accelerators, new protocol, network processing arch.)

• IP Situation: patent is expected to the end of the POC grant. All IP will be concentrated in Singapore.

• Market: Brand-new servers for search engines and cloud computing (hosting), webmail hosting.• Competitors: Brawny cores companies – Intel, AMD, Sun (Oracle)

• Disruptive innovation: Deep specialization and customization of the chip through the transport system.• Revenue model: revenue should come from selling the chips or licensing the technology.

Summary






5. Summary

6. Annex


Key Technology: Transport SystemGoogle bottlenecks:

New transport system features:Feature Description

Hardware support for a custom protocol HW parsers implementation to support the data-structures for Google, Hadoop, Yandex, etc.

Hardware acceleration Decoders, CryptographyNew instructions SYNC code detection, fast protocol parsing, fast CRC

Different transport scenarios To support a very large system scalability

Bottleneck DescriptionOptimization of searching an index Instantly searching an index of more than 100

million gigabytesSerialization, Remote Procedure Call, Data Exchange

Fast communication between the servers.

Data compression, cryptography Large resource consuming tasks.

Scalability to 10 million servers X10 number of servers increase in Google in few years


Key Technology: protocol

* Protocol will natively support <Key,Value> frames for hardware parsing** Protocol will natively support the data structures for different search engines (Google, Yandex,..)

New protocol to effectively manage the (Key, Value) frames HBaseFile

KV_FRAME

BLOCKSHbase File structure

Applications

Parsers (Google, Hadoop)

Network abstraction layer

Device Drivers

Protocol

Protocol will be supported by HW/SW parsers

KV_FRAME

BLOCKS

HBase File


GP-NP architecture (part)

Header

Data

Header

Data

Header

Data

Header

Data

ApplicationProcessing

Table1

Table2

Table3

Table4

FilterFilter

SecurityDriver

GPNP

SystemMemory

SecurityFirmware

Security

Data

Data

SystemMemory

MapReduce,Dremel,Index Search,Speech recognition,Webmail

SystemMemory