Energy versus Output Quality of Non-volatile Writes in Intermittent Computing
CoRR(2024)
Abstract
We explore how to improve the energy performance of battery-less Internet of
Things (IoT) devices at the cost of a reduction in the quality of the output.
Battery-less IoT devices are extremely resource-constrained energy-harvesting
devices. Due to erratic energy patterns from the ambient, their executions
become intermittent; periods of active computation are interleaved by periods
of recharging small energy buffers. To cross periods of energy unavailability,
a device persists application and system state onto Non-Volatile Memory (NVM)
in anticipation of energy failures. We purposely control the energy invested in
these operations, representing a major energy overhead, when using
Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM) as NVM. As a
result, we abate the corresponding overhead, yet introduce write errors. Based
on 1.9+ trillion experimental data points, we illustrate whether this is a
gamble worth taking, when, and where. We measure the energy consumption and
quality of output obtained from the execution of nine diverse benchmarks on top
of seven different platforms. Our results allow us to draw three key
observations: i) the trade-off between energy saving and reduction of output
quality is program-specific; ii) the same trade-off is a function of a
platform's specific compute efficiency and power figures; and iii) data
encoding and input size impact a program's resilience to errors. As a
paradigmatic example, we reveal cases where we achieve up to 50
energy consumption with negligible effects on output quality, as opposed to
settings where a minimal energy gain causes drastic drops in output quality.
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