Using GZIP Data Compression to Reduce Power Consumption in IoT Devices

IoT Energy Savings with GZIP IP
Using GZIP Data
Compression to Reduce
Power Consumption
in IoT Devices
Meredith Lucky
VP of Sales
CAST, Inc.
December 2016
www.cast-inc.com

Second Generation IoT
The First Generation was trying to get a product
to market ASAP.
The Second Generation is about product
differentiation.
Low power
Processing power in edge devices
Reduced cost (less memory, smaller devices)

A Typical IoT Node
Low Energy Consumption is a key factor
CPU(s)
SRAM
Wireless Protocol Accel.
(e.g. Baseband, Link Layer)
Accelerators
NVM (e.g. Flash, EEPROM)
RF AFE (e.g. 802.11ah, Zigbee etc)
SENSORS/ACTUATORS
NVM Controller
Peripherals
BATTERY/ENERGYHARVESTINGDEVICE

Where is Energy Consumed?
RF Transceivers and NVMs
are major energy consumers
CPU(s)
SRAM
Accelerators
SENSORS/ACTUATORS
NVM Controller
Peripherals
MCU (no NVM or RF)
0.5–5mA
Low-power RF
10–30mA
Low-power Serial Flash
2–25mA
Low-power CPU
0.1–1 nAmA

Reducing the Energy Consumed
via Compression
NVM: Firmware Compression
Less on-chip data to transfer = lower energy (and
less time) to boot/wake-up
NVM: Data Storage Compression
Less off-chip data = faster R/W operations = lower
energy
RF: Tx/Rx Data Compression
Less data to send/receive = lower RF active time =
lower communication energy

IoT Node with GZIP Accelerator
CPU(s)
SRAM
Peripherals &
Accelerators
SENSORS/ACTUATORS
NVM Controller
GZIP/GUNZIP Accel.
GZIP Compression
Standard for interoperability
Hardware
Acceleration
Lower power consumption,
less processor overhead,
low latency
Uses
NVM: Firmware decompression while code shadowing
NVM: data compression for local data storage
RF: data compression at application level or in networking stack

Expected Energy Gains
Depends on compression performance (ratio), which
depends on Data Characteristics and Compression
Algorithm Parameters
Basic GZIP Compression Parameters:
Static or Dynamic Huffman
Static: smaller silicon
Dynamic: higher compression
History Window
Smaller: smaller silicon
Larger: higher compression
Low silicon overhead = Static Huffman and relatively
small History Window

How Compressible is
Wireless Data?
IoT Devices typically
Exchange a set of measurements/sensed data
Are able to receive commands, and
May allow remote access via an HTML page
Data they transmit resembles text files,
spreadsheets, and HTML
These all compress nicely to levels of 3:1 even
for Static Huffman and Small History Windows

How Compressible Is Firmware?
Three examples:
 FreeRTOS Port and Sensor Control app on BA22-DE
 Cygnal FreeRTOS Port and demo app on 8051
 InterNiche Tech. TCPI/IP and HTTP Stacks on Cortex-M3
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Sta c Huffman, 1024
History
Sta c Huffman, 2048
History
Sta c Huffman, 4096
History
2.17
2.35 2.44
2.05 2.14 2.23
1.83 1.93 2.01
CompressionRao

Energy and Boot Time Savings
from Firmware Compression
GZIP Assumption: Static Huffman, 2KB History
NVM Assumptions: 1.8V Serial, 5mA Read Current, 50MHz Read Clock
Code Size in kBytes Required NVM Size
System #1
(8051)
System #2
(BA2)
System #3
(ARM)
System #1
(8051)
System #2
(BA2)
System #3
(ARM)
Uncompressed
Code 25.5 161 985 256kbits 2Mbit 8Mbits
Compressed Code 10.9 76 511 128kbits 1Mbit 4Mbits
Savings 57.25% 52.80% 48.12% 54.25% 50.00% 50.00%
Boot Time in msec Boot Power in mA - sec
System #1
(8051)
System #2
(BA2)
System #3
(ARM)
System #1
(8051)
System #2
(BA2)
System #3
(ARM)
Uncompressed
Code 3.98 25 154 0.02 0.13 0.77
Compressed Code 1.7 12 80 0.01 0.06 0.4
Savings 57.29% 52.00% 48.05% 57.25% 52.80% 48.12%
averages
Code Size
52.72%
NVM Size
51.42%
Boot Time
52.45%
Boot
Power
52.73%

Data
Storage
Servers &
Gateways
Move analytics from cloud to edge for faster response and
bandwidth limited applications -> Requires local data
storage instead of sending data to remote server

Local Data Storage for Analytics
Store data from many edge sensor devices. Wake-up
server for periodic analytic processing
No longer just simple log data, but much larger
imagery and natural language data
Compression minimizes the amount of NVM, but also
power of read/write operations
for 2KB Page for Block of 64 pages = 128kB
Access time
us/page
Power
uJ/page
Power
uJ/block
Read 130.9 4.72 302
Write 405.9 38.94 2,492
Power example in Micron SLC NAND

Energy Saving VS Duty Cycles
Example IoT node without compression
Total Energy (mJ/hour) 328.257 40.278 11.480 8.600
Example IoT node with compression (Compression Ratio: 2:1)
Duty Cycle (sec per hour) 5.000 0.500 0.050 0.005
RF Active (Tx) Energy (mJ/hour) 150.00 15.00 1.50 0.15
RF Idle Energy (mJ/hour) 7.19 7.20 7.20 7.20
MCU Active Energy (mJ/hour) 10.00 1.00 0.10 0.01
MCU Idel Energy 1.08 1.08 1.08 1.08
GZIP Active Energy (mJ/hour) 0.91 0.09 0.01 0.00
GZIP Idle Energy (mJ/hour) 0.00 0.00 0.00 0.00
Total (mJ/hour) 169.175 24.370 9.889 8.441
Net Savings (mJ/hour) 159.082 15.908 1.591 0.159
% Energy Saved 48.5% 39.5% 13.9% 1.8%
*
*
*
* Energy decreased 50%

Conclusion
Compression can be used to significantly decrease
the energy consumption in IoT Nodes
Firmware Compression for devices using code shadowing
Compression of local Data for edge analytics
Compression of Data exchanged over wireless links
Parameters that need to be evaluated
Compression algorithm and parameters
Compressibility of data
System’s energy profile (duty cycle)
GZIP/GUNZIP IP cores available from CAST are
perfectly suitable for IoT Nodes

GZIP Cores From CAST
Low latency:
from 20 cycles FIFO latency
Low silicon requirements:
~22kGates for gzip or gunzip (Static Huffman
and History Windows sizes up to 4096 Bytes)
Low power:
160 MBytes/sec at just 10 MHz
Easy integration:
AXI-Streaming (AXI-ST), or AHB Interfaces

Using GZIP Data Compression to Reduce Power Consumption in IoT Devices

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Editor's Notes