1. Low Latency Analytics and Edge Connected Vehicles with RapidIO Interconnect Devashish Paul, Director Strategic Marketing devashish.paul@idt.com June 2016 devashish.paul@idt.com © Integrated Device Technology

2. IDT Company Overview Devashish Paul, Director Strategic Marketing 2 Mixed-signal application-specific solutions #1 Serial Switching – 100% 4G Infrastructure with RapidIO #1 Memory Interface – Industry Leader DDR4 #1 Silicon Timing Devices – Broadest Portfolio 800+ Issued and Pending Patents Worldwide #1 Serial Switching – 100% 4G Infrastructure with RapidIO #1 Memory Interface – Industry Leader DDR4 #1 Silicon Timing Devices – Broadest Portfolio 800+ Issued and Pending Patents Worldwide

3. Low Latency Analytics and Edge Connected Vehicles with RapidIO Devashish Paul, Director Strategic Marketing 3 Agenda  Computing Trends/Distributed Computing  RapidIO 20-50 Gbps Technology  100 ns Low Latency Analytics Architectures  CERN OpenLab collaboration  Analytics examples with low latency RapidIO  Connected Vehicles Use Case

4. 5G Mobile Edge Computing + Edge Connected Appliances Devashish Paul, Director Strategic Marketing 4 The Network is the Data Center Ecommerce Fleet Management Semi Autonomous Vehicles Traffic management Low Latency Energy Efficient Analytics Workloads At Network Edge RapidIO IEEE 1588 Timing RF Products Memory Interface Retimers Sensors

5. Devashish Paul, Director Strategic Marketing 5 The Network is the Data Center Ubiquitous Computing: IDT connects, synchronizes, times and makes sense of the human-and-machine connected world

6. Network and Data Center Convergence Devashish Paul, Director Strategic Marketing 6 Apps moving from data center to co-locate with access node (base station or wired access node) Supporting real time communication to mobile devices (phones, cars IOT) Tight time synchronization between apps running on distributed servers and in data center Need low latency interconnect Edge Computing an essential element of 5G Rollouts Wired and Wireless Access nodes Servers/ Analytics

7. PPC GPU Data Center Servers Cloud RAN 4G BTS Towards 5G/MEC  Co-located CPU and Acceleraters Devashish Paul, Director Strategic Marketing 7 Mobile Edge Computing

8. RapidIO Interconnect combines the best attributes of PCIe ® and Ethernet in a multi-processor fabric Ethernet PCIe Scalability Low Latency Hardware Terminated Guaranteed Delivery Ethernet in software only Clustering Fabric Needs Lowest Deterministic System Latency Scalability Peer to Peer / Any Topology Embedded Endpoints Energy Efficiency Cost per performance HW Reliability and Determinism Lowest Deterministic System Latency Scalability Peer to Peer / Any Topology Embedded Endpoints Energy Efficiency Cost per performance HW Reliability and Determinism Devashish Paul, Director Strategic Marketing 8

9. Heterogeneous Edge Analytics with RapidIO Heterogeneous compute workloads No protocol termination CPU cycles Energy efficiency 20 to 50 Gbps embedded interconnect Scalable Fat node connect multiple boards in Edge Appliance Push Video encode/decode, streaming, rendering and analytics into the network edge Devashish Paul, Director Strategic Marketing 9 Board leve RapidIO Inter- Connect Fabric FPGA/GPU accelerators FPGA/GPU accelerators CPU Storage Cable / connector Backplane RapidIO Rack Scale Interconnect Fabric Other nodes RapidIO Backplane Interconnect Fabric Edge Analytics require low latency compute For HPC – like compute Video server

10. 10/20/40/50 Gbps per port – 6.25/10/12.5 Gps lane 100+ Gbps interconnect in definition Hardware termination at PHY layer: 3 layer protocol Lowest Latency Interconnect ~ 100 ns Inherently scales to large system with 1000’s of nodes Over 15 million RapidIO switches shipped > 2xEthernet (10GbE) Over 110 million 10-20 Gbps ports shipped 100% 4G interconnect market share Devashish Paul, Director Strategic Marketing 10

11. Supported by Large Eco-system 02/09/16 Devashish Paul, Director Strategic Marketing 11 © 2015 RapidIO.org

12. RapidIO Ecosystem and Market Progression Performance evolves for multiple system platform generations 2000 2002 2004 2006 2008 2010 2012 2014 2016+ 1 st Gen Standard 2 nd Gen Standard 3 rd Gen Standard 4th Gen Standard Space Interconnect Bridging Data Center Computing Storage RapidIO Gen3 (10xN) released with path to 25 Gbaud Devashish Paul, Director Strategic Marketing 12

13. NASA Space Interconnect Standard Next Generation Spacecraft Interconnect Standard RapidIO selected from Infiniband / Ethernet /FiberChannel / PCIe NGSIS members: BAE, Honeywell, Boeing, Lockheed- Martin, Sandia Cisco, Northrup-Grumman, Loral, LGS, Orbital Sciences, JPL, Raytheon, AFRL 13

14. Open Compute Project High Performance Computing 02/09/16 Devashish Paul, Director Strategic Marketing 14 IDT Co-Chairs Opencompute.org HPC Initiative

15. RapidIO.org ARM64 bit Scale Out Group 10s to 100s cores & Sockets ARM AMBA® protocol mapping to RapidIO protocols -AMBA 4 AXI4/ACE mapping to RapidIO protocols -AMBA 5 CHI mapping to RapidIO protocols Migration path from AXI4/ACE to CHI and future ARM protocols Supports Heteregeneous computing Support Wireless/HPC/Data Center Applications Latency Scalability Hardware Termination Energy Efficiency Source – www.rapidio.org, Linley Processor confwww.rapidio.org

16. 16 Better Link Utilization RapidIO offers better Link Utilization – RapidIO Link Rate: 14.46 Gbps (Gen2 x4) without encoding overhead >90% Utilization Link Rate 14.46 Gbps >90% Utilization Link Rate 14.46 Gbps

17. 17 Low CPU Overhead RapidIO keeps CPU available for actual User Application – RapidIO keeps CPU overhead below 1% – IDT RapidIO 20G PCIE-RapidIO NIC and Intel x86 Ethernet – Ethernet Intel Driver Software with TCP/IP – Intel x86 CPU x5560 and Intel X520 2x10GbE PCIe-Ethernet NIC RapidIO CPU Overhead less than 1% CPU Capacity available for User Processing RapidIO CPU Overhead less than 1% CPU Capacity available for User Processing

18. 18 Significant Power Savings RapidIO offers Significant Power Savings – Low Power PCIe-RapidIO NIC with low CPU overhed – IDT RapidIO 20G PCIE-RapidIO NIC and Intel x86 Ethernet – Ethernet Intel Driver Software – Intel x86 CPU x5560 and Intel X520 2x10GbE PCIe-Ethernet NIC RapidIO saves ~15W/CPU Power Saving around 1.2 KW per Rack (80 socket) RapidIO saves ~15W/CPU Power Saving around 1.2 KW per Rack (80 socket)

19. 20 Gbps RapidIO2 Analytics Kit 19 PCIe-RapidIO NIC (Tsi721) PCIe-RapidIO NIC (Tsi721) RapidIO Switch Unit (CPS1432/CPS1848) RapidIO Switch Unit (CPS1432/CPS1848) Driver Software General Available May 20, 2016 Fabric Management TCP/IP Device Drivers Hardware (NIC/Switch) Remote Memory Access User Space Kernel Space

20. RapidIO Interconnect at CERN OpenLab 20

21. RapidIO at CERN Openlab: LHC and Data Center 21 RapidIO Low latency interconnect fabric Heterogeneous computing Large scalable multi processor systems Desire to leverage multi core x86, ARM, GPU, FPGA, DSP with uniform fabric Desire programmable upgrades during operations before shut downs

22. CERN OpenLab Root Analytics on RapidIO 22

23. CERN OpenLab Event Building Use Case on RapidIO 23

24. 24

25. Analytics Optimized 100ns 50 Gbps Switch RXS2448 –600 Gbps Full-Duplex Serial RapidIO ® Switch –50 Gbps per port –33 x 33 mm package –48 lanes at 12.5 Gbps –Up to 24 Serial RapidIO Ports –RapidIO Specification (Rev 3.2) Compliant RXS1632 –400 Gbps Full-Duplex Serial RapidIO Switch –50 Gbps per port –29 x 29 mm package –32 lanes at 12.5 Gbps –Up to 16 Serial RapidIO Ports –RapidIO Specification (Rev 3.2) Compliant Clocking & Reset Power Mgmt JTAGI2C Maintenance Buffer and Control Scheduler and Switch Fabric Stack Port0/1 SerDes Stack Port0/1 SerDes Stack Port0/1 SerDes Stack Port0/1 SerDes StackPort0/1 SerDes StackPort0/1 SerDes StackPort0/1 SerDes StackPort0/1 SerDes StackPort0/1 SerDes StackPort0/1 SerDes StackPort0/1 SerDes StackPort0/1 SerDes ©2016. IDT. 50 Gbps per port | 300mW per 10 Gbps data | 100ns latency Devashish Paul, Director Strategic Marketing 25

26. Local Inter- Connect Fabric Cable / Connector CPU/ Accelerator CPU/ Accelerator Storage I/O Hub I/O Hub 20 to 50 Gbps RapidIO Ports Scalable Low Latency Analytics Server Fat Node Devashish Paul, Director Strategic Marketing 26 20 to 50 Gbps embedded ports 300 mW per 10 Gbps 100 ns latency Distributed switching Direct connection to BBU CPU/ Accelerator CPU/ Accelerator Storage I/O Hub I/O Hub Edge Node with I/O Hub Local Inter- Connect Fabric FPGA/GPU CPU Storage Cable / Connector Edge Node Native Interconnect 20 to 50 Gbps RapidIO Ports RapidIO Top of Rack Switching

27. Edge Analytics Systems APPLICATION ISSUES SOLVED ● 50 Gbps per port with 95% link utilization ● 100 ns latency ● Power efficient 300 mW per 10 Gbps RXS SoC FPGA DSP Connector RXS SoC FPGA DSP Connector RXS SoC FPGA DSP Connector RXS BBU Basestation RXS Fabric Cluster for Rack Switching C-RAN Backplane RXS CPU FPGA/ GPU RXS Server Backplane RXS CPU FPGA/ GPU Server RXS CPU FPGA/ GPU Server Edge Computing Appliance Distributed low latency switching Optimized for needs of Edge Analytics Devashish Paul, Director Strategic Marketing 27

28. Low Latency Switch for Edge Computing Scale Out Devashish Paul, Director Strategic Marketing 28 38 x 20 Gbps ports Sub 200W switching power Support 42U Rack level scale out Available Now 0.75 Tbps 1 U 19 Inch 100 ns Switch With 20 Gbps ports Roadmap to 4.8 Tbps 2U 100 ns Switch With 50 Gbps ports 96 x 50 Gbps ports Sub 400W switching power Supports redundant ports to 42U rack and intra rack scale out 2H 2016 5G|Mobile Edge Computing |HPC| Video Analytics | Low Latency Financial Trading

29. 29 12x 50 Gbps per port Switch Silicon

30. X86 Edge Based Analytics Appliance Devashish Paul, Director Strategic Marketing 30 Key Components: 19 inch 1U ToR 38x20 port ToR switch 19 inch 1U networking server with built in 320 Gbps RapidIO Switching Quad Intel Broadwell Sockets 4 TB storage in 8 bays 320 Gbps built in switching in Video Server 100ns latency per switch hop Up to 160 sockets per rack with any to any connectivity TCP/IP software driver for RapidIO + IDT Open Source Software Available Q2 2016 Analytics Server and ToR RapidIO|x86| Low Latency | Energy Efficient

31. GPU Acceleration Nodes 02/09/16 Devashish Paul, Director Strategic Marketing 31 Computing 4 x Nvidia Tegra K1 GPU per Compute slot 16x per 19 Inch 1U server Analytics acceleration or cloud based rendering of gaming content Interconnect Fabric RapidIO Switching + 4x RapidIO Gen2 NIC Performance 1.28 Tflops/Compute (4 GPU) 140 Gbps Switching Fabric 100nsec RapidIO switching latency Low Latency GPU Analytics Module http://www.gocct.com/sheets/AG/photograph/hires/aga1xm1d.jpg http://www.gocct.com/sheets/AG/aga1xm1d.htm

32. GPU+ARM RapidIO Video Analytics RapidIO Switch Appliance Low Power ARM+GPU Cluster with RapidIO ~29 frames/sec/node ~100 ns RapidIO switching latency > TFlops Processing/node ~4W per GPU node

33. Edge Video Analytics: IBM Power8/Nvidia Tesla/RapidIO Switching Devashish Paul, Director Strategic Marketing 33 Roadmap to 4.8 Tbps 2U 100 ns Switch With 50 Gbps ports Building the OpenPower Edge Node Low latency RapidIO ToR Switching 38x20 20 Gbps 100 ns IDT RapidIO switch latency, 95% link utilization Multi socket Power8 servers 38 sockets per rack any to any compute for video analytics Option to add Nvidia Tesla class GPU for acceleration per Power8 Node IDT Versaclock5 Timing

34. Edge Social Data Analytics Analyze User Impressions on World Cup Final 2014 (Germany/Argentina) – HPAC Lab project to analyze World Cup 2014 twitter data using Hadoop and visualize using Tableau public on HPAC Platform Devashish Paul, Director Strategic Marketing 34 Orange Silicon Valley

35. 5G Lab Germany: Edge Analytics for Autonomous Vehicles Devashish Paul, Director Strategic Marketing 35 Low Latency Edge Computing Key to Tactile Internet

36. Automotive Sensor and Sensor Fusion 36 Keys to Next-Generation Automotive Designs IDT now offers even a greater array of solutions Automotive sales channel provides immediate leverage for existing IDT products

37. 5G Lab Germany: Edge Analytics for Autonomous Vehicles Network Edge Connected Vehicles –HPC like workload and network edge GPU/x86/ARM/Open Power based Analytics Low latency RapidIO Fabric –In vehicle sensor fusion in real time with low latency –Leverage OCP Innovations (Edge Appliance and ToR) –Multi Vehicle Sensors analysed at network edge by “Supercomputing at the Edge” Devashish Paul, Director Strategic Marketing 37 Autonomous Vehicle Video Analytics/Object Recognition Deep Learning/Object Analytics

38. Connected Vehicle Edge Analytics Workloads 38 Offload Use Cases Fleet Management Municipal Self Driving Snow Removal Police Services Traffic management Weather/Hazard avoidance Low Latency Energy Efficient Analytics Workloads At Network Edge Many workloads HPC-like requiring low latency Requires mission critical reliability at the appliance level Leverages 5G Network and associated handoffs between 5G nodes RapidIO is Key Enabler for this Edge Use Case

39. RapidIO Analytics and Connected Vehicles Low Latency Servers 20 to 50 Gbps RapidIO interconnect with 100ns latency Heterogeneous Computing X86, OpenPower, ARM GPU and FPGA assist Push Video encode/decode, streaming, rendering and analytics into the network edge Devashish Paul, Director Strategic Marketing 39 Board level RapidIO Inter- Connect Fabric FPGA/GPU accelerators FPGA/GPU accelerators CPU Storage Cable / connector Backplane RapidIO Rack Scale Interconnect Fabric Other nodes RapidIO Backplane Interconnect Fabric HPC like workloads In data center, Edge and Vehicles Video server

40. Backup 40

41. Packet Structure – PHY Layer PHY Layer Fields

42. Packet Structure – Transport Layer Transport Layer Fields

43. Packet Structure – Logical Layer Logical Layer Fields