Preparing for the Internet of Things 50 Trillion Gigabyte ... · Preparing for the Internet of...
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Preparing for the Internet of Things 50 Trillion Gigabyte Challenge Pat McGarry Ryft Systems, Inc.
Transcript of Preparing for the Internet of Things 50 Trillion Gigabyte ... · Preparing for the Internet of...
Preparing for the Internet of Things 50
Trillion Gigabyte Challenge
Pat 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
