Presentation RuChip Pte Ltd
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Transcript of Presentation RuChip Pte Ltd
RuChip Pte Ltd SingaporeAnton GerasimovEvgeny KovalevVolkov Mikhail
Alexander BlinovKonstantine Stowolosow
© Ruchip Pte Ltd confidential 1
Ultra Low-powerSearching Microprocessor
© Ruchip Pte Ltd confidential 2
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
© Ruchip Pte Ltd confidential 3
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
© Ruchip Pte Ltd confidential 4
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
© Ruchip Pte Ltd confidential 5
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?
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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?
© Ruchip Pte Ltd confidential 7
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
© Ruchip Pte Ltd confidential 8
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. Summary
6. Annex
© Ruchip Pte Ltd confidential 9
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
© Ruchip Pte Ltd confidential 10
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 Pte Ltd confidential 11
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
© Ruchip Pte Ltd confidential 12
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
© Ruchip Pte Ltd confidential 13
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
© Ruchip Pte Ltd confidential 14
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. Summary
6. Annex
RuChip Supply Chain
© Ruchip Pte Ltd confidential 15
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
© Ruchip Pte Ltd confidential 16
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.
© Ruchip Pte Ltd confidential 17
Competitors
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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
© Ruchip Pte Ltd confidential 19
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:
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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. Summary
6. Annex
© Ruchip Pte Ltd confidential 21
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
© Ruchip Pte Ltd confidential 22
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. Summary
6. Annex
© Ruchip Pte Ltd confidential 23
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
© Ruchip Pte Ltd confidential 24
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
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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