Get ready to dive into the exciting world of IoT data processing! 🌐📊 Join us for a thought-provoking webinar on "Processing: Turning IoT Data into Intelligence" hosted by industry visionary Deepak Shankar, founder of Mirabilis Design. Discover how to harness the potential of IoT devices by strategically choosing processors that optimize power, performance, and space. In this engaging session, you'll explore key insights: ✅ Impact of processor architecture on Power-Performance-Area optimization ✅ Enabling AI and ML algorithms through precise compute and storage requirements ✅ Future trends in IoT hardware innovation ✅ Strategies for extending battery life and cost prediction through system design Don't miss the chance to learn how to leverage a single IoT Edge processor for multiple applications and much more. This is your opportunity to gain a competitive edge in the evolving IoT landscape.

1. Sponsored by:
EETimes University Course:
Mastering IoT Design: Sense, Process, Connect
Day 2: Processing: Turning IoT Data into Intelligence
Presented by:
Deepak Shankar
Founder and Vice President, Technology
Mirabilis Design Inc.
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2. Sponsored by:
Our Speaker:
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Deepak Shankar
Founder and Vice President, Technology
Mirabilis Design Inc.

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Agenda
Introduction to processing at
Edge Computing
Trends
Challenges
Case Study and Analysis
Conclusion
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Enabling Data Center Connected Devices
• 50 billion IoT devices generate 79.4ZB requires
– Huge bandwidth
– Massive and expensive cloud processing
– 99.999% reliability
– Cybersecurity
– Variable response latency
• Solution: Edge Computing
– Process and analyze data locally
– Make instant decisions
• Applications that benefit from Edge Computing
– Safety, health care, security, autonomous vehicles,
smart cities
– Manufacturing, VR, AR, stream services, customer care
– Smart homes
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Architecture of Edge Computing
• Edge computing is at the
outermost edge of network
• Sensor data is collected,
filtered, compressed, and
encrypted locally and a
small quantity is sent to the
cloud
• Network transfer can be
done in batch or offline
• Increases the number of
processing layers
• Server location is critical
• Tracking and diagnostic is
critical and adds layers
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Trends
• Edge gateway separates the processing and end-point
– Increases the battery life of the connected devices
– Add more sensors and trackers at the device
• Example
– Smart electricity meters generate lots of measurements at short intervals
– Using data from temperature, light and other sensors, Edge Computing can respond to
environment changes quickly, reducing operational cost and increasing comfort
• Filtering edge data reduces cloud transfers, which increases battery life
• Example
– Mercedes AMG F1 team filters less valuable telemetry and transfers relevant data for offline
processing
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Trends
• There is no “single” edge
– Depending on the application, the edge architecture will be different
– Manufacturing plant [Edge servers and gateways aggregate multiple
servers and devices in a distributed location]
– Telecommunications provider [architectures break down into a provider far
edge, a provider access edge, and a provider aggregation edge]
• Edge computing doesn’t need to be connected
– An edge device running autonomously in a hard-to-reach place like a rural
mining site can keep running when a connection drops
– When connection resumes, the system can sync and transfer data
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Computing Trends
• Edge is getting “foggy”
– Fog computing adds a new filtering layer
– Example
• Temperature sensor generates data every one second
• With fog layer, data that meet the metrics are sent to cloud
• Advanced communication and AI technology enable mass
connection
– Wi-Fi 6 and 5G/6G covers most edge devices
– About 99.4% of the physical objects are still unconnected
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Challenges
• Latency (Response times)
– Applications, like the health care, financial transactions, and automotive
cannot tolerate latency
– Stringent requirements need to be met and thus the components need to be
selected to meet such needs
• Bandwidth
– Applications, like Smart surveillance systems and traffic management,
require significant bandwidth
– Buffer overflow, retransmissions can severely affect application performance
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Challenges
• Edge has same cyber risk as any other local network
server
– “AT&T Cybersecurity Insights report: Securing the Edge” found
that 74% of businesses have been compromised
– Sniffing attacks, DDoS, supply chain attacks, etc.
• Power
– Edge servers rely on battery as their primary power source
– IoT device batteries are not easily replaceable
• Fire alarm sensors, underwater sensors, wildlife tracking devices,
medical implants, space and satellite devices
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Designing Edge Computing Requires Early Architecture
Planning
• Early system model can eliminate bottlenecks and measure
– Processing requirements
– Expected application latency
– Required bandwidth for seamless operation
– Power profile of the edge devices
– Behavior of the system under different failure conditions and threats
• To enable this, the system modeling solution requires IP libraries and
simulators for hardware, software, and network
– VisualSim Architect from Mirabilis Design ticks all the boxes

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Why is System Performance Exploration Required?
Scheduling/Arbitration
proportional
share
WFQ
static
dynamic
fixed priority
EDF
TDMA
FCFS
Communication Templates
Architecture # 1 Architecture # 2
Computation Templates
DSP
AI
GPU
DRAM
CPU
FPGA
m
E
DSP
TDMA
Priority
EDF
WFQ
RISC
DSP
LookUp
Cipher
AI DS
P
CPU
GP
U
mE DD
R
static
Which architecture is better suited
for our application?
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Block Diagram of Data-Enabled Application
Device 1
AFE
ARM/
RISC-V
core
BLE/
Wifi/ 5G
Radio
Transceiv
er
Network
Hub
[Edge Gateways
+ Fog layers]
Data
Center

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IoT to Data Center Model
• Power consumption of each
device
• Optimal HW and SW
configuration to meet the end-to-
end application timing deadline
• Performance with Big data and
fast data workloads
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Architect Hardware-Software of Edge Device
DRAM
Display
IO
A
M
B
A
A
X
I
B
u
s
CPU
GPU
Display
Ctrl
P
C
I
e
Video Camera SRAM
Packet
• System Overview
– Camera： 30fps, VGA corresponds
– CPU： Multi-core ARM Cortex-A53 1.2GHz
– GPU： 64Cores(8Warps×8PEs), 32Threads,
1GHz
– DisplayCtrl： DisplayBuffer 293,888Byte
– SRAM： SDR, 64MB, 1.0GHz
– DRAM： DDR3, 64MB, 2.4GHz
Explore at the board- and semiconductor-level to size uP/GPU, memory bandwidth and bus/switch configuration
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Conducting Architecture Trade-off
• By changing the amount of video input data (packet numbers), observe the SRAM -> DRAM
transfer performance and examine the upper limit performance of the video input that the
system can tolerate. 210 packets/s
12ms
21 packets/s
414us
300 packets/s
• 250 packets/s is the system limit
• With 300 packets/s, simulation cannot
be executed due to FIFO buffer
overflow.
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Compare Edge Compute System using ARM vs RISC-V
Processors
Using ARM Cores Using RISC-V Cores
NOTE: Depending on the application profile run, the performance and power observed would
vary
• Compare application latency with and without custom instructions tailored for AI
and data filtering
• Average power consumption in different scenarios
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Selecting Network Interface to Data Center
Using 1000 Mbps links 1000 Mbps link + Edge Processing (Single- and multi-core)
Sizing of network resources to Edge devices is important to get optimal performance
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Bluetooth (BLE) Power State Transition (FSM)
• Depending on the
abstraction required,
the subsystems can
be built with great
details
• Here, Bluetooth
module has been
modeled with all the
state transition
details
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Resource usage
extremely high due
to continued high
traffic levels
Security against DDoS Attack on Edge and Networks
A DDoS attack involves multiple
connected devices, collectively
known as a botnet, which are used to
overwhelm a target vehicle with
fake/malicious traffic.
• Virtually emulating the system behavior using DDoS attack technique
• Simulation methodology is designed to test automotive systems for cybersecurity
and safety that are under development
• Undertake Risk Assessment on network simulation to predict how the interaction of
restrictions and behavior may affect overall network hygiene
• Moreover, testing multiple potential settings can aid in finding a near-optimal
configuration for restrictions
• The above design analyzes the overall throughput and latency on the network under
attack.
High Delay in processing
request result in low
throughput
What is
DDoS?
DDoS Attack
Outcomes ?
Analyze the
System Design
and find best
optimal
configuration
High Resource Usage
due to Attacks on On-
Board Units. Unable to
discern between normal
and attack traffic
OB
U
Diagnostic
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Failure and Cybersecurity Threat Analysis
Normal Operation Under Attack
Effective
throughput is
reduced and
significant bursts
in response times
are observed
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Evaluate Bottlenecks and Security Threats
Stable
Response
Time
Malicious Devices are Blacklisted
Without
Firewall
With
Firewall
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Edge Computing Architecture is Key for Data Intelligence
• System exploration must be performed during the planning stage
• Distribution of processing to IoT, edge, fog, and data center must
be based on the application requirements
• Edge, Network, and IoT System specification must be fully
validated for both power-performance and failure-security
• Data size, processing needs, and software tasks must be
determined in advance
• System must be scalable for future requirements
• Edge computing is the Future

24. Sponsored by:
About Mirabilis Design
Software Company based in Silicon Valley
Integrates Model-based Systems Engineering with the electronics
development flow
Development and Support Centers
USA, India, China, South Korea, Japan and Europe
VisualSim Architect - Modeling and Simulation Software
Graphical modeling, multi-domain simulator, system-level IP, analysis tools and
open API
Market Segments
Semiconductors, Automotive and, Aerospace and Defense
Design Enablement
Architecture trade-offs, system validation, early functional testing and
communication
Networking
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Q&A
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