820 research outputs found
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data at the boundary of the on-chip analytics engine is a way to address data
security issues. To cope with the combined workload of analytics and encryption
in a tight power envelope, we propose Fulmine, a System-on-Chip based on a
tightly-coupled multi-core cluster augmented with specialized blocks for
compute-intensive data processing and encryption functions, supporting software
programmability for regular computing tasks. The Fulmine SoC, fabricated in
65nm technology, consumes less than 20mW on average at 0.8V achieving an
efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to
25MIPS/mW in software. As a strong argument for real-life flexible application
of our platform, we show experimental results for three secure analytics use
cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN
consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with
secured remote recognition in 5.74pJ/op; and seizure detection with encrypted
data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE
Transactions on Circuits and Systems - I: Regular Paper
Data Collection and Utilization Framework for Edge AI Applications
As data being produced by IoT applications continues to explode, there is a
growing need to bring computing power closer to the source of the data to meet
the response time, power dissipation and cost goals of performance-critical
applications in various domains like the Industrial Internet of Things (IIoT),
Automated Driving, Medical Imaging or Surveillance among others. This paper
proposes a data collection and utilization framework that allows runtime
platform and application data to be sent to an edge and cloud system via data
collection agents running close to the platform. Agents are connected to a
cloud system able to train AI models to improve overall energy efficiency of an
AI application executed on an edge platform. In the implementation part, we
show the benefits of FPGA-based platform for the task of object detection.
Furthermore, we show that it is feasible to collect relevant data from an FPGA
platform, transmit the data to a cloud system for processing and receiving
feedback actions to execute an edge AI application energy efficiently. As
future work, we foresee the possibility to train, deploy and continuously
improve a base model able to efficiently adapt the execution of edge
applications
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