1. Pat McGarry Ryft Systems, Inc. Closing Keynote Harnessing the Flood of IoT Data With Heterogeneous Computing at the Edge

2. SOURCE: IDC WORLDWIDE IoT TAXONOMY, 2015, WORLDWIDE INTERNET OF THINGS FORECAST, 2015–2020 The Internet of Things (IoT) will trigger the single biggest IT shift since the Internet. 4 25+ 50 50 $1.7+ BILLION BILLION BILLION TRILLION TRILLION 4 25+ 50 50 $1.7+ BILLION BILLION BILLION TRILLION TRILLION Connected Applications Devices GBs Data Market People Connected Applications Devices GBs Data Market People

3.  Real-time insights as events occur, close to the source of data  Analysis of data from a range of IoT devices— video, mobile, batch stores, etc.—together  Ultra small & efficient analytics infrastructure  Easy to deploy, use & maintain systems  Low operational costs  No security or performance trade-offs IoT is exacerbating the widening data analytics technology divide. REQUIREMENTS  Persistent compute/IO/storage bottlenecks  Data analyzed in silos  Data movement & ETL delays  Sprawling inefficient analytics infrastructures  Persistent data privacy & security issues REALITY

4. The reason? Actionable intelligence from IoT is trapped in analysis platforms built on 70-year old architectures.

5. Heterogeneous Computing is the answer… SOURCES: BLOOMBERG BUSINESS, THE PLATFORM Heterogeneous computing refers to systems that use more than one kind of processor or cores. These systems gain performance or energy efficiency not just by adding the same type of processors, but by adding dissimilar processors, usually incorporating specialized processing capabilities to handle particular tasks.

6. …because optimal performance & efficiency demands the right “engine” for the job. CPUsFPGA General purpose computing Sequential in nature Non-deterministic performance Interrupts Memory allocation Problems are broken into a sequence of operations and processed serially Not general purpose Purpose built algorithms Can be reprogramed via firmware Best at data-heavy analysis Search, fuzzy search, image and video analysis, deep learning Inherently massively parallel Can execute many hardware-parallel operations in one clock cycle More output with less power Can complete the same problem at 100X the performance of x86 GPUs Some general purpose computing Can excel at certain complex algorithms Best for image rendering, some image analysis Generally more parallel than CPUs, since GPUs have more cores Generally more power efficient than CPU

7. Performance CPUFPGAGPU Open API CPUFPGAGPU ….but we still need an open, easy-to-use approach with a business-centric, compute-agnostic open API.

8. Performance A compute-agnostic Open API example in C:

9. Performance Non-programmatic interfaces are just as simple: $ ryftprim -n 1 -p fhs -f input.txt -od results.txt -q "(RAW_TEXT CONTAINS \"Michelle\")" -w 16 -d 2 -oi index.txt -v

10. Open APIs must become prevalent, for all analytics.

11.  Real-time analysis of the range of data types—video, image, & text— whether IoT-generated or traditionally generated, required to shape a problem or experience at the edge was not possible  Behavioral data could not be analyzed along with traditional data  Data movement, ETL & indexing bogged down the network & slowed analysis  Localized analysis consumed valuable space & resources  Real-time analysis of the range of data types—video, image, & text— whether IoT-generated or traditionally generated, required to shape a problem or experience at the edge was not possible  Behavioral data could not be analyzed along with traditional data  Data movement, ETL & indexing bogged down the network & slowed analysis  Localized analysis consumed valuable space & resources ANALYSIS AT THE EDGE Make informed business decisions by analyzing all your data, immediately.

12.  Instant analysis of on-location video, text, & sensor data together as it happens allows unprecedented personalization  Immediate insight into newly arriving IoT data correlated to legacy data  Data can be analyzed locally to increase responsiveness & reduce network load  No data movement or ETL = no barriers to real-time insights  Small, easy-to-maintain infrastructure brings powerful analytics to the network edge at a fraction of the space, maintenance & energy  Solve business problems using business-centric, open APIs instead of developing complex computing algorithms  Instant analysis of on-location video, text, & sensor data together as it happens allows unprecedented personalization  Immediate insight into newly arriving IoT data correlated to legacy data  Data can be analyzed locally to increase responsiveness & reduce network load  No data movement or ETL = no barriers to real-time insights  Small, easy-to-maintain infrastructure brings powerful analytics to the network edge at a fraction of the space, maintenance & energy  Solve business problems using business-centric, open APIs instead of developing complex computing algorithms Heterogeneous computing and open APIs eliminate edge computing bottlenecks. THE SOLUTION

13. Enabling the Internet of Things with real-time insights into massive data volume & velocity at the edge. Ultra-high-speed, compact, high-efficiency, & heterogeneous Ryft ONE accelerates the delivery of valuable insights from large, dynamic, & diverse data sets at its source exclusively using open APIs.

14. Questions? Visit Ryft’s IoT Slam virtual exhibit. Download white papers, analyst reports, & videos. Pat McGarry pat.mcgarry@ryft.com www.ryft.com