1,980 research outputs found
Using the DiaSpec design language and compiler to develop robotics systems
A Sense/Compute/Control (SCC) application is one that interacts with the
physical environment. Such applications are pervasive in domains such as
building automation, assisted living, and autonomic computing. Developing an
SCC application is complex because: (1) the implementation must address both
the interaction with the environment and the application logic; (2) any
evolution in the environment must be reflected in the implementation of the
application; (3) correctness is essential, as effects on the physical
environment can have irreversible consequences. The SCC architectural pattern
and the DiaSpec domain-specific design language propose a framework to guide
the design of such applications. From a design description in DiaSpec, the
DiaSpec compiler is capable of generating a programming framework that guides
the developer in implementing the design and that provides runtime support. In
this paper, we report on an experiment using DiaSpec (both the design language
and compiler) to develop a standard robotics application. We discuss the
benefits and problems of using DiaSpec in a robotics setting and present some
changes that would make DiaSpec a better framework in this setting.Comment: DSLRob'11: Domain-Specific Languages and models for ROBotic systems
(2011
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
Depth sensing is a critical function for robotic tasks such as localization,
mapping and obstacle detection. There has been a significant and growing
interest in depth estimation from a single RGB image, due to the relatively low
cost and size of monocular cameras. However, state-of-the-art single-view depth
estimation algorithms are based on fairly complex deep neural networks that are
too slow for real-time inference on an embedded platform, for instance, mounted
on a micro aerial vehicle. In this paper, we address the problem of fast depth
estimation on embedded systems. We propose an efficient and lightweight
encoder-decoder network architecture and apply network pruning to further
reduce computational complexity and latency. In particular, we focus on the
design of a low-latency decoder. Our methodology demonstrates that it is
possible to achieve similar accuracy as prior work on depth estimation, but at
inference speeds that are an order of magnitude faster. Our proposed network,
FastDepth, runs at 178 fps on an NVIDIA Jetson TX2 GPU and at 27 fps when using
only the TX2 CPU, with active power consumption under 10 W. FastDepth achieves
close to state-of-the-art accuracy on the NYU Depth v2 dataset. To the best of
the authors' knowledge, this paper demonstrates real-time monocular depth
estimation using a deep neural network with the lowest latency and highest
throughput on an embedded platform that can be carried by a micro aerial
vehicle.Comment: Accepted for presentation at ICRA 2019. 8 pages, 6 figures, 7 table
HALLS: An Energy-Efficient Highly Adaptable Last Level STT-RAM Cache for Multicore Systems
Spin-Transfer Torque RAM (STT-RAM) is widely considered a promising
alternative to SRAM in the memory hierarchy due to STT-RAM's non-volatility,
low leakage power, high density, and fast read speed. The STT-RAM's small
feature size is particularly desirable for the last-level cache (LLC), which
typically consumes a large area of silicon die. However, long write latency and
high write energy still remain challenges of implementing STT-RAMs in the CPU
cache. An increasingly popular method for addressing this challenge involves
trading off the non-volatility for reduced write speed and write energy by
relaxing the STT-RAM's data retention time. However, in order to maximize
energy saving potential, the cache configurations, including STT-RAM's
retention time, must be dynamically adapted to executing applications' variable
memory needs. In this paper, we propose a highly adaptable last level STT-RAM
cache (HALLS) that allows the LLC configurations and retention time to be
adapted to applications' runtime execution requirements. We also propose
low-overhead runtime tuning algorithms to dynamically determine the best
(lowest energy) cache configurations and retention times for executing
applications. Compared to prior work, HALLS reduced the average energy
consumption by 60.57% in a quad-core system, while introducing marginal latency
overhead.Comment: To Appear on IEEE Transactions on Computers (TC
Handling the Subclassing Anomaly with Object Teams
Java software or libraries can evolve via subclassing. Unfortunately, subclassing may not properly
support code adaptation when there are dependencies between classes. More precisely, subclassing in
collections of related classes may require reimplementation of otherwise valid classes. This problem is defined as
the subclassing anomaly, which is an issue when software evolution or code reuse is a goal of the programmer
who is using existing classes. Object Teams offers an implicit fix to this problem and is largely compatible with the
existing JVMs. In this paper, we evaluate how well Object Teams succeeds in providing a solution for a complex,
real world project. Our results indicate that while Object Teams is a suitable solution for simple examples, it does
not meet the requirements for large scale projects. The reasons why Object Teams fails in certain usages may
prove useful to those who create linguistic modifications in languages or those who seek new methods for code
adaptation
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