Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
5G Network, GPU and FPGA
Notes Sharing
Richard T. Kuo
Intent
• Highlight few trends affect telecom infrastructure architecture:
• Digital Society
• Big Data and Machine Learnin...
20161004 CC 4.0 SA, NC 3
20161004 CC 4.0 SA, NC 4
Trends
• Digitalization
• Virtualization
• Physical -> digital -> data driven
• IoE -> data 4V (volume, variety, velocity,...
20161004 CC 4.0 SA, NC 6
What Should 5G Bring
20161004 CC 4.0 SA, NC 7
20161004 CC 4.0 SA, NC 8
5G Use Cases
20161004 CC 4.0 SA, NC 9
5G Main Enablers
• Dynamic RAN provides a RAN that can adapts to rapid changes in user
needs and the mix of the generic 5G...
Data Process at Edge
20161004 CC 4.0 SA, NC 11
from: http://ubiquity.acm.org/article.cfm?id=2822875
Edge Computing
• Pushes applications, data and computing power (services) to the
logical extremes of a network.
• Replicat...
20161004 CC 4.0 SA, NC 13
Edge Computing
from: http://ubiquity.acm.org/article.cfm?id=2822875
20161004 CC 4.0 SA, NC 14
Advantages of Edge Computing
• Decrease the data volume that must be moved, the consequent
traffic, and the distance the d...
5G Ecosystem
from 5G-PPP-5G-Architecture-WP-For-public-consultation.pdf
20161004 CC 4.0 SA, NC 16
KT – Network Architecture Evolution 4G to 5G
from http://www.netmanias.com/en/?m=attach&no=13956
20161004 CC 4.0 SA, NC 17
KT - Network Architecture Evolution 4G to 5G
from http://www.netmanias.com/en/?m=attach&no=13955
20161004 CC 4.0 SA, NC 18
Edge Node Architecture
(need design improvement, this is only a place holder)
20161004 CC 4.0 SA, NC 19
from: http://ubiqu...
20161004 CC 4.0 SA, NC 20
Graphics Processing Unit (GPU)
• A GPU is a computer chip that performs rapid mathematical
calculations, primarily for the...
CPU vs GPU
from http://www.electronicspecifier.com/communications/vivante-es-design-magazine-gpus-the-next-must-have
20161...
Parallel Computing and Streaming
The Landscape of Parallel Computing Research: A View from Berkeley
The 13 application are...
Current VPP Hardware Acceleration
from https://fd.io/technology
20161004 CC 4.0 SA, NC 24Q QS1`
Field Programmable Gate Arrays (FPGAs)
• Field Programmable Gate Arrays
(FPGAs) are semiconductor devices
that are based a...
FPGA Applications Partial List
from: http://www.xilinx.com/training/fpga/fpga-field-programmable-gate-array.htm
• Aerospac...
GPU and FPGA Considerations
• GPU has good penetration on ML community. It needs to overcome
big inertia to get people mov...
Observations and Interpretations
• Add flexible Edge Computing Nodes for 5G network.
• Use high computing power and custom...
Supplementary Material
20161004 CC 4.0 SA, NC 29
5G
1000xMobile Data
Volumes
10x-100x
Connected Devices 5xLower Latency
10x-100x
End-user Data Rates
10x
Battery Life for L...
5G Requirements
Business Requirements
• Massive broadband (xMBB) that delivers
gigabytes of bandwidth on demand <-
velocit...
Comparison between 1g, 2g, 3g, 4g and 5g
20161004 CC 4.0 SA, NC 32
20161004 CC 4.0 SA, NC 33
20161004 CC 4.0 SA, NC 34
5G mMBB Requirements and Technology Enablers
• Software Defined Radio (SDR) -> multiple radio technologies.
• Massive mult...
20161004 CC 4.0 SA, NC 36
Logic Gates
Truth Table FPGA
20161004 CC 4.0 SA, NC 37
Binary Adder
from: http://cpuville.com/adder.htm
Truth Table Gates for Adder
20161004 CC 4.0 SA, NC 38
FPGA Development Board
Mercury FPGA
From: http://www.micro-nova.com/mercury/
Terasic SoCKit Altera Cyclone V + Dual Core A...
Programming FPGA
20161004 CC 4.0 SA, NC 40
20161004 CC 4.0 SA, NC 41
20161004 CC 4.0 SA, NC 42
20161004 CC 4.0 SA, NC 43
20161004 CC 4.0 SA, NC 44
Upcoming SlideShare
Loading in …5
×

5g, gpu and fpga

2,024 views

Published on

Study notes: the possible impact of GPU, FPGA on 5G network.

Published in: Mobile
  • DOWNLOAD FULL MOVIE, INTO AVAILABLE FORMAT ......................................................................................................................... ......................................................................................................................... ,DOWNLOAD FULL. MOVIE 4K,FHD,HD,480P here { https://tinyurl.com/yybdfxwh }
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

5g, gpu and fpga

  1. 1. 5G Network, GPU and FPGA Notes Sharing Richard T. Kuo
  2. 2. Intent • Highlight few trends affect telecom infrastructure architecture: • Digital Society • Big Data and Machine Learning • List some enablers for evolving demand: • Edge Computing • Graphic Process Unit(GPU) • Field Programmable Gate Array (FPGA) • Discuss some supporting architecture options: • Customize Nodes • Network Slicing 20161004 CC 4.0 SA, NC 2
  3. 3. 20161004 CC 4.0 SA, NC 3
  4. 4. 20161004 CC 4.0 SA, NC 4
  5. 5. Trends • Digitalization • Virtualization • Physical -> digital -> data driven • IoE -> data 4V (volume, variety, velocity, veracity) -> analytics, optimization, ML • Mobilization • Connectivity • Unconnected -> mobile -> mobile optimized • Increase system scope/domain/complexity/interactions • Place -> People -> Devices • Automation -> self-directed/managing/healing • Mobility • Time => anytime • space => anywhere • data => more available information/inference 20161004 CC 4.0 SA, NC 5
  6. 6. 20161004 CC 4.0 SA, NC 6
  7. 7. What Should 5G Bring 20161004 CC 4.0 SA, NC 7
  8. 8. 20161004 CC 4.0 SA, NC 8
  9. 9. 5G Use Cases 20161004 CC 4.0 SA, NC 9
  10. 10. 5G Main Enablers • Dynamic RAN provides a RAN that can adapts to rapid changes in user needs and the mix of the generic 5G services. • Ultra-Dense Networks, • Moving Networks, • Devices acting as temporary as access nodes, • D2D communication for both access and backhaul. • Lean System Control Plane (LSCP) provides new lean signaling/control information and allows separation of data and control information and support large variety of devices with very different capabilities. • Localized Contents and Traffic Flows allow offloading, aggregation and distribution of real-time and cached content. • Spectrum Toolbox contains a set of enablers (tools) to allow 5G systems to operate under different regulatory framework and share enablers. 20161004 CC 4.0 SA, NC 10
  11. 11. Data Process at Edge 20161004 CC 4.0 SA, NC 11 from: http://ubiquity.acm.org/article.cfm?id=2822875
  12. 12. Edge Computing • Pushes applications, data and computing power (services) to the logical extremes of a network. • Replicates fragments of information across distributed networks. • Place customized nodes (SDR, SDN, NFV, …) closer to the client whenever it is possible. 20161004 CC 4.0 SA, NC 12
  13. 13. 20161004 CC 4.0 SA, NC 13
  14. 14. Edge Computing from: http://ubiquity.acm.org/article.cfm?id=2822875 20161004 CC 4.0 SA, NC 14
  15. 15. Advantages of Edge Computing • Decrease the data volume that must be moved, the consequent traffic, and the distance the data must go, thereby reducing transmission costs, shrinking latency. • Reduces or eliminates the network bottlenecks. • Fast response times. • Reduce security risk by reducing the non-essential data travels into network core, only necessary data forward for analysis. • Deeper insights, with privacy control: Analyze sensitive data locally instead of sending it to the cloud for analysis. 20161004 CC 4.0 SA, NC 15
  16. 16. 5G Ecosystem from 5G-PPP-5G-Architecture-WP-For-public-consultation.pdf 20161004 CC 4.0 SA, NC 16
  17. 17. KT – Network Architecture Evolution 4G to 5G from http://www.netmanias.com/en/?m=attach&no=13956 20161004 CC 4.0 SA, NC 17
  18. 18. KT - Network Architecture Evolution 4G to 5G from http://www.netmanias.com/en/?m=attach&no=13955 20161004 CC 4.0 SA, NC 18
  19. 19. Edge Node Architecture (need design improvement, this is only a place holder) 20161004 CC 4.0 SA, NC 19 from: http://ubiquity.acm.org/article.cfm?id=2822875
  20. 20. 20161004 CC 4.0 SA, NC 20
  21. 21. Graphics Processing Unit (GPU) • A GPU is a computer chip that performs rapid mathematical calculations, primarily for the purpose of rendering images. • A GPU generally has a large number of slow and weak processors (lower operating frequency, lower number of registers, simpler ALU's etc.) • GPU's come strapped with lots of memory and generally have high memory bandwidth to support the hundreds of small processors that make up the GPU. • GPUs are special purpose and can compute vector math, matrix math, pixel transforms and rendering jobs about 10-100x faster than the equivalent CPU performance as all these tasks are embarrassingly parallel. 20161004 CC 4.0 SA, NC 21
  22. 22. CPU vs GPU from http://www.electronicspecifier.com/communications/vivante-es-design-magazine-gpus-the-next-must-have 20161004 CC 4.0 SA, NC 22
  23. 23. Parallel Computing and Streaming The Landscape of Parallel Computing Research: A View from Berkeley The 13 application areas where OpenCL can be used 1. Dense Linear Algebra 2. Sparse Linear Algebra 3. Spectral Methods 4. N-Body Methods 5. Structured Grids 6. Unstructured Grids 7. Monte Carlo 8. Combinational Logic 9. Graph traversal 10. Dynamic Programming 11. Backtrack and Branch + Bound 12. Construct Graphical Models 13. Finite State Machine 14. … 20161004 CC 4.0 SA, NC 23
  24. 24. Current VPP Hardware Acceleration from https://fd.io/technology 20161004 CC 4.0 SA, NC 24Q QS1`
  25. 25. Field Programmable Gate Arrays (FPGAs) • Field Programmable Gate Arrays (FPGAs) are semiconductor devices that are based around a matrix of configurable logic blocks (CLBs) connected via programmable interconnects. • FPGAs can be reprogrammed to desired application or functionality requirements after manufacturing. 20161004 CC 4.0 SA, NC 25
  26. 26. FPGA Applications Partial List from: http://www.xilinx.com/training/fpga/fpga-field-programmable-gate-array.htm • Aerospace & Defense - Radiation-tolerant FPGAs along with intellectual property for image processing, waveform generation, and partial reconfiguration for SDRs. • ASIC Prototyping - ASIC prototyping with FPGAs enables fast and accurate SoC system modeling and verification of embedded software • Audio - Xilinx FPGAs and targeted design platforms enable higher degrees of flexibility, faster time-to-market, and lower overall non-recurring engineering costs (NRE) for a wide range of audio, communications, and multimedia applications. • Automotive - Automotive silicon and IP solutions for gateway and driver assistance systems, comfort, convenience, and in-vehicle infotainment. - Learn how Xilinx FPGA's enable Automotive Systems • Broadcast - Adapt to changing requirements faster and lengthen product life cycles with Broadcast Targeted Design Platforms and solutions for high- end professional broadcast systems. • Consumer Electronics - Cost-effective solutions enabling next generation, full-featured consumer applications, such as converged handsets, digital flat panel displays, information appliances, home networking, and residential set top boxes. • Data Center - Designed for high-bandwidth, low-latency servers, networking, and storage applications to bring higher value into cloud deployments. • High Performance Computing and Data Storage - Solutions for Network Attached Storage (NAS), Storage Area Network (SAN), servers, and storage appliances. • Medical - For diagnostic, monitoring, and therapy applications, the Virtex FPGA and Spartan® FPGA families can be used to meet a range of processing, display, and I/O interface requirements. • Video & Image Processing - Xilinx FPGAs and targeted design platforms enable higher degrees of flexibility, faster time-to-market, and lower overall non-recurring engineering costs (NRE) for a wide range of video and imaging applications. • Wired Communications - End-to-end solutions for the Reprogrammable Networking Linecard Packet Processing, Framer/MAC, serial backplanes, • and more 20161004 CC 4.0 SA, NC 26
  27. 27. GPU and FPGA Considerations • GPU has good penetration on ML community. It needs to overcome big inertia to get people move away from GPUs CUDA. • FPGAs uses less power, but new NVIDIA chips use as few as 10-15 watts for a teraflop. • Verification of complex designs implemented on FPGA is a big challenge. In contrast testing and validating CUDA code is relatively easier. • Fast digital design, no wait to obtaining a target chip. • The design can be implemented on the FPGA and tested at once. • FPGAs are good for prototyping. Design change change can be absorbed in field. 20161004 CC 4.0 SA, NC 27
  28. 28. Observations and Interpretations • Add flexible Edge Computing Nodes for 5G network. • Use high computing power and customized SoC+FPGA for BTS/eNode- B, and form a cluster/cloud to share resources. • Accelerate performance with Field Programmable Gate Array. • Adapt Software Defined Radio and dynamic RAN. • Leverage GPUs for distributed data analytics and ML. • Consider Micro-HPC (High Performance Computing) data center for deployment. 20161004 CC 4.0 SA, NC 28
  29. 29. Supplementary Material 20161004 CC 4.0 SA, NC 29
  30. 30. 5G 1000xMobile Data Volumes 10x-100x Connected Devices 5xLower Latency 10x-100x End-user Data Rates 10x Battery Life for Low Power Devices Source: METIS Evolution Towards 2020 4G3G2G © Telefonaktiebolaget LM Ericsson 2015 | Ericsson June 2015
  31. 31. 5G Requirements Business Requirements • Massive broadband (xMBB) that delivers gigabytes of bandwidth on demand <- velocity • Massive machine-type communication (mMTC) that connects billions of sensors and machines <- variety + volume • Critical machine-type communication (uMTC) that allows immediate feedback with high reliability and enables for example remote control over robots and autonomous driving. <- velocity + veracity Technology Requirements • 1-10Gbps connections to end points in the field (i.e. not theoretical maximum) • 1 millisecond end-to-end round trip delay (latency) • 1000x bandwidth per unit area • 10-100x number of connected devices • (Perception of) 99.999% availability • (Perception of) 100% coverage • 90% reduction in network energy usage • Up to ten year battery life for low power, machine-type devices 20161004 CC 4.0 SA, NC 31
  32. 32. Comparison between 1g, 2g, 3g, 4g and 5g 20161004 CC 4.0 SA, NC 32
  33. 33. 20161004 CC 4.0 SA, NC 33
  34. 34. 20161004 CC 4.0 SA, NC 34
  35. 35. 5G mMBB Requirements and Technology Enablers • Software Defined Radio (SDR) -> multiple radio technologies. • Massive multiple-input and multiple-output (MIMO) antennas -> data throughput and capacity • Dynamic RAN 20161004 CC 4.0 SA, NC 35
  36. 36. 20161004 CC 4.0 SA, NC 36
  37. 37. Logic Gates Truth Table FPGA 20161004 CC 4.0 SA, NC 37
  38. 38. Binary Adder from: http://cpuville.com/adder.htm Truth Table Gates for Adder 20161004 CC 4.0 SA, NC 38
  39. 39. FPGA Development Board Mercury FPGA From: http://www.micro-nova.com/mercury/ Terasic SoCKit Altera Cyclone V + Dual Core A9 From: http://www.cnx-software.com/2013/08/15/249-terasic-sockit- development-kit-features-altera-cyclone-v-sx-dual-core-a9-fpga-soc/ 20161004 CC 4.0 SA, NC 39
  40. 40. Programming FPGA 20161004 CC 4.0 SA, NC 40
  41. 41. 20161004 CC 4.0 SA, NC 41
  42. 42. 20161004 CC 4.0 SA, NC 42
  43. 43. 20161004 CC 4.0 SA, NC 43
  44. 44. 20161004 CC 4.0 SA, NC 44

×