RuChip presentation for Skolkovo

1. Ultra Low-power
Searching Microprocessor
RuChip Pte Ltd Singapore
Anton Gerasimov
Evgeny Kovalev
Volkov Mikhail
Alexander Blinov
Konstantine Stowolosow
© Ruchip Pte Ltd confidential 1

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. Summary
6. Annex
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3. Digital Universe growth
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
More information = more processing power:
In 2009 there was more than 35 million servers all
over the world (US$60B)
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4. Search servers market forecast
Every third server in the world is used for information search
Today the search servers market size is US$20B a year
(source: IDC 2008, Gartner 2009)
Text + Text + Images
Web Search service evolution: Text
Images + Video
Demand for new servers: ~1.5M units a year, and growing
Servers
units
50M Video recognition
and indexing
40M
30M Images
recognition
20M Real-time and indexing
text search
10M
2011 2016 2020 Year
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5. The problem
Today’s x86 processors are not fast enough to index the
information as it is being generated
Energy factor: the necessity to
cope with increasing demand for
CPU cycles results in huge power
consumption
And what if the cost of a kWh in
5 years will be $1, not $0.11?
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”.
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6. So what is the solution?
New specialized processor with
Ultra-low power consumption
High performance due to the use of specialized functional
blocks to alleviate the bottlenecks/overheads of a search
engine:
Data serialization, RPC
Data 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.
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7. Who our customers will be?
Generally: Search start-ups funding over the last years
Staurtup Search
Distributed <Key,Value> stores and Year
Engine
Total VC Funding
services based on them Yahoo! Search
2004 A9.com Amazon.com project
Hakai $21 million
MSN Search Microsoft project
Specifically: Ask.com
GoodSearch Yahoo project
2005
Search engines: Google, Yahoo, Kosmix
Like.com
$55 million
$48 million
new search start-ups SearchMe $21 million
Live Search Microsoft project
2006
ChaCha $58 million
Social networks, social graphs Wikiseek SearchMe project
Wikia Search
2007 Blackle.com Google project
Hosting for Cloud providers Mahalo $58 million
Powerset $21 million
Future hosting (Amazon-like, e- Viewzi
Cuil $33 million
2008
commerce) Boogami
VADLO
2009 Bing Microsoft project
Average $40 million
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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 8

9. Solution: a new architecture
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
The key functions of GPNP:
Data processing
Packets parsing
Packets routing
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10. Solving highlighted problems
Problem Solution (RuChip)
The “Power Wall” (power consumption) ARM/Atom “mobile cores” (x10 better)
Performance bottlenecks (Google) : New transport system:
1) Data serialization, compression, 1) GPNP architecture
cryptography 2) New protocol to support data structures
2) Instant large index searching 3) Custom instructions extensions, HW acceleration.
Portability issue: software stack adaptation HW/SW adaptation layer
NP NP NP
GP GP GP
LOAD BALANCER
Software NP NP NP
GP GP GP
Stack
New protocol
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11. RuChip Key Added Value
NoC
Front-end
design
Back-end
cores design
ASIP
Chip
design
Transport
Goya micro- System
processor
The Transport System is the crucial link in the entire value chain
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12. Design Targets
Cost (ASP) Cost (ASP)
Existing
RuChip
Power Performance Power Performance
Consumption Consumption
* Performance validation: Goya chip prototype vs. Amazon Elastic Compute Cloud
** Power consumption estimation: CAD (Synopsys, Cadence)
*** Workload is a Nutch/Hadoop framework, embedded version
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13. POC Objectives
Work packages structure
Objective Description
Design of the transport system (NP) – Assess the power consumption and
ARCH + RTL + System Model the best/worst performance POC
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)
Transport system concept validation:
Protocol parsing; Custom instructions; Encoders/decoders; Security; Transport channels
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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 Pte Ltd confidential 14

15. RuChip Supply Chain
Data centers
state & corporate
Goya Search engines
Software global, regional,
specialized
Server makers
Goya &
microprocessor System integrators
Search Engine
Goya specialized
board New search
engines,
Start-ups
Boards/chipset Licensing
makers
Social Networks
Facebook,
MySpace
Goya = Google + Yahoo
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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 Web
based on an image pattern
2020 Text + Images + Video $50B People search the Web for video pattern
1-st Goya chip generation: text indexing
2-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 2,100,000
2015 $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
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17. Competitors
There are similar technologies out there which can be
compared to us:
Multicores (picoChip, Ambric, Tilera), Cisco Quantum, Octeon
processor (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.
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18. Disruptive innovation
Deep specialization and customization of the chip
Customization of the Transport System (NP)
for the tasks and data structures of Google
NP NP NP
GP GP GP
Main algorithms:
Large batch processing LOAD BALANCER
(MapReduce)
Real time indexing NP NP NP
(Dremel)
GP GP GP
Software: NP – optimization of the GP-GP communication
NOSQL Frameworks GP – general purpose part (can be either ARM or Intel)
Commercial software stacks
(Cloud)
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19. Go-To-Market Strategy
Product
1-st stage: Transport IP (NP): ASIC + boards
2-nd stage: Multicore chip (GPNP) + boards
Marketing channels
Integrators (Novell,..)
Direct marketing
CASE:
Target markets
B2B: Search engines, Hosting for NOSQL,
Cloud providers, Webmail hosting, Social
networks
B2C: Mini-search engines (real-time)
100+ Software applications
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20. 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 20

21. Summary
Problem
• 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
Proposed Solution
• The solution: ARM/Atom - based servers and • IP Situation: patent is expected to the end of
optimization of the distributed communication system the POC grant. All IP will be concentrated in
• Key challenges: Optimization of the transport Singapore.
processing system (custom instructions, HW
accelerators, new protocol, network processing arch.)
Business Model
• Market: Brand-new servers for search engines • Disruptive innovation: Deep specialization and
and cloud computing (hosting), webmail hosting. customization of the chip through the transport system.
• Competitors: Brawny cores companies – Intel, • Revenue model: revenue should come from selling
AMD, Sun (Oracle) the chips or licensing the technology.
© Ruchip Pte Ltd confidential 21

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 22

23. Key Technology: Transport System
Google bottlenecks:
Bottleneck Description
Optimization of searching an index Instantly searching an index of more than 100
million gigabytes
Serialization, Remote Procedure Call, Data Fast communication between the servers.
Exchange
Data compression, cryptography Large resource consuming tasks.
Scalability to 10 million servers X10 number of servers increase in Google in few years
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, Cryptography
New instructions SYNC code detection, fast protocol parsing, fast CRC
Different transport scenarios To support a very large system scalability
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24. Key Technology: protocol
New protocol to effectively manage the (Key, Value) frames
HBase
HBase File File
KV_FRAME
KV_FRAME
BLOCKS
Hbase File structure
Protocol
Applications
BLOCKS
Device Drivers
Parsers (Google, Hadoop)
Protocol will be supported by HW/SW parsers Network abstraction layer
* Protocol will natively support <Key,Value> frames for hardware parsing
** Protocol will natively support the data structures for different search engines (Google, Yandex,..)
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25. GP-NP architecture (part)
NP GP
Header
Data Table1
Filter
Header Filter Application
Data Processing
Table2
Security MapReduce,
Driver Dremel,
Table3 Index Search,
Speech recognition,
Webmail
Header Table4
Data Data
Security
Header Firmware Data
Data
Security System
System Memory System
Memory Memory
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