SLIDE 1
The IoT 50 Trillion GB Challenge: The Largest Opportunity & Threat Since the Internet
SOURCE: WIKIBON BIG DATA VENDOR REVENUE & MARKET FORECAST 2011-2026
Preparing for the Internet of Things 50 Trillion Gigabyte Challenge - - PowerPoint PPT Presentation
Preparing for the Internet of Things 50 Trillion Gigabyte Challenge - - PowerPoint PPT Presentation
Preparing for the Internet of Things 50 Trillion Gigabyte Challenge Pat McGarry Ryft Systems, Inc. The IoT 50 Trillion GB Challenge: The Largest Opportunity & Threat Since the Internet SOURCE: WIKIBON BIG DATA VENDOR REVENUE & MARKET
SLIDE 2
SLIDE 3
- 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 Data
What You Need to Know About IoT Data and Its Impact on Information Infrastructure
SLIDE 4
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
SLIDE 5
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
SLIDE 6
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.”
SLIDE 7
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
- perations &
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
SLIDE 8
Performance
CPU FPGA GPU Open API CPU FPGA GPU
Requirements for Success: Compute-agnostic API
SLIDE 9
The Future Is Intelligence at the Network Edge
Find the right data–even when it’s incomplete–whenever & wherever you need it.
EDGE NODE EDGE NODE EDGE NODE
SLIDE 10