Preparing for the Internet of Things 50 Trillion Gigabyte ChallengePat McGarryRyft Systems, Inc.

The IoT 50 Trillion GB Challenge: The Largest Opportunity & Threat Since the Internet

SOURCE: WIKIBON BIG DATA VENDOR REVENUE & MARKET FORECAST 2011-2026

Variety: an explosion of types and formats Structure: unstructured and messy Volume: too much for most platforms to analyze Velocity: fast and furious Value: expires quickly Location: widely distributed

Data Dynamics: Critical Differences in IoT DataWhat You Need to Know About IoT Data and Its Impact on Information Infrastructure

Common Barriers to IoT’s Popular Use Cases

Real-time insights as events occur, close to the source of data

Advanced-scale performance & storage to analyze data from a variety of IoT devices

Compact & efficient infrastructure

Easy to deploy, use & maintain ecosystems

Minimal disruption to existing ecosystems

Low operational costs

No security or performance trade-offs

Analysis slowed by data ETL & movement

Persistent compute, I/O & storage bottlenecks

Data types that must be analyzed in silos

Sprawling, inefficient analytics infrastructures

Frequent software ecosystem updates

Persistent data privacy & security issues

WHAT ENTERPRISES NEED TO THRIVE WHAT ENTERPRISES HAVE TODAY

Real-time Image Recognition

Fraud Detection

Biometric Recognition

Voice Recognition

Behavior Monitoring

The Heart of Popular IoT Use Cases

Optical Character Recognition

Similarity Search

Financial Compliance

Malicious Pattern Matching

Cyber Security

Thriving in the IoT Era: Fast Data Analysis Powered by New Hybrid FPGA/x86 Compute Architectures

“Systems built on GPUs and FPGAs will function more like human brains that are particularly suited to be applied to deep learning and other pattern-matching algorithms that smart machines use. FPGA-based architecture will allow further distribution of algorithms into smaller form factors, with considerably less electrical power in the device mesh, thus allowing advanced machine learning capabilities to be proliferated into the tiniest IoT endpoints, such as homes, cars, wristwatches and even human beings.

— David Cearley, Gartner

“Intel’s $16.7 Billion Altera Deal Is Fueled by Data Centers.”

“Microsoft Supercharges Bing Search with Programmable Chips.”

Hybrid Compute: The Right Engine for the Job

CPU FPGA General purpose

computing Sequential in nature Nondeterministic

performance —Interrupts —Memory allocation

Problems broken into sequential operations & processed serially

Not general purpose — Purpose built algorithms— Can be reprogrammed via firmware

Data analysis— Search, fuzzy search, image and video analysis, deep learning

Inherently 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/CPU

GPU Some general purpose

computing Excels at

mathematically complex algorithms

Image rendering, some image analysis

Generally more parallel than CPUs, since GPUs have more cores

Generally more power efficient than CPU

Performance

CPU FPGAGPU

Open API

CPU FPGAGPU

Requirements for Success: Compute-agnostic API

The Future Is Intelligence at the Network EdgeFind the right data–even when it’s incomplete–whenever & wherever you need it.

EDGE NODE

EDGE NODE

EDGE NODE

Questions?Visit the Ryft IoT SLAM booth

Pat McGarry pat.mcgarry@ryft.comwww.ryft.com