3,861 research outputs found
Ono: an open platform for social robotics
In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform
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
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
Successful materials innovations can transform society. However, materials
research often involves long timelines and low success probabilities,
dissuading investors who have expectations of shorter times from bench to
business. A combination of emergent technologies could accelerate the pace of
novel materials development by 10x or more, aligning the timelines of
stakeholders (investors and researchers), markets, and the environment, while
increasing return-on-investment. First, tool automation enables rapid
experimental testing of candidate materials. Second, high-throughput computing
(HPC) concentrates experimental bandwidth on promising compounds by predicting
and inferring bulk, interface, and defect-related properties. Third, machine
learning connects the former two, where experimental outputs automatically
refine theory and help define next experiments. We describe state-of-the-art
attempts to realize this vision and identify resource gaps. We posit that over
the coming decade, this combination of tools will transform the way we perform
materials research. There are considerable first-mover advantages at stake,
especially for grand challenges in energy and related fields, including
computing, healthcare, urbanization, water, food, and the environment.Comment: 22 pages, 3 figure
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