4,812 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
A Domain Specific Approach to High Performance Heterogeneous Computing
Users of heterogeneous computing systems face two problems: firstly, in
understanding the trade-off relationships between the observable
characteristics of their applications, such as latency and quality of the
result, and secondly, how to exploit knowledge of these characteristics to
allocate work to distributed computing platforms efficiently. A domain specific
approach addresses both of these problems. By considering a subset of
operations or functions, models of the observable characteristics or domain
metrics may be formulated in advance, and populated at run-time for task
instances. These metric models can then be used to express the allocation of
work as a constrained integer program, which can be solved using heuristics,
machine learning or Mixed Integer Linear Programming (MILP) frameworks. These
claims are illustrated using the example domain of derivatives pricing in
computational finance, with the domain metrics of workload latency or makespan
and pricing accuracy. For a large, varied workload of 128 Black-Scholes and
Heston model-based option pricing tasks, running upon a diverse array of 16
Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both
the makespan and accuracy are generally within 10% of the run-time performance.
When these models are used as inputs to machine learning and MILP-based
workload allocation approaches, a latency improvement of up to 24 and 270 times
over the heuristic approach is seen.Comment: 14 pages, preprint draft, minor revisio
Embedded Machine Learning: Emphasis on Hardware Accelerators and Approximate Computing for Tactile Data Processing
Machine Learning (ML) a subset of Artificial Intelligence (AI) is driving the industrial
and technological revolution of the present and future. We envision a world with smart
devices that are able to mimic human behavior (sense, process, and act) and perform
tasks that at one time we thought could only be carried out by humans. The vision
is to achieve such a level of intelligence with affordable, power-efficient, and fast hardware
platforms. However, embedding machine learning algorithms in many application domains
such as the internet of things (IoT), prostheses, robotics, and wearable devices is an ongoing
challenge. A challenge that is controlled by the computational complexity of ML algorithms,
the performance/availability of hardware platforms, and the application\u2019s budget (power
constraint, real-time operation, etc.). In this dissertation, we focus on the design and
implementation of efficient ML algorithms to handle the aforementioned challenges. First, we
apply Approximate Computing Techniques (ACTs) to reduce the computational complexity of
ML algorithms. Then, we design custom Hardware Accelerators to improve the performance
of the implementation within a specified budget. Finally, a tactile data processing application
is adopted for the validation of the proposed exact and approximate embedded machine
learning accelerators.
The dissertation starts with the introduction of the various ML algorithms used for
tactile data processing. These algorithms are assessed in terms of their computational
complexity and the available hardware platforms which could be used for implementation.
Afterward, a survey on the existing approximate computing techniques and hardware
accelerators design methodologies is presented. Based on the findings of the survey, an
approach for applying algorithmic-level ACTs on machine learning algorithms is provided.
Then three novel hardware accelerators are proposed: (1) k-Nearest Neighbor (kNN) based
on a selection-based sorter, (2) Tensorial Support Vector Machine (TSVM) based on Shallow
Neural Networks, and (3) Hybrid Precision Binary Convolution Neural Network (BCNN).
The three accelerators offer a real-time classification with monumental reductions in the
hardware resources and power consumption compared to existing implementations targeting
the same tactile data processing application on FPGA. Moreover, the approximate accelerators
maintain a high classification accuracy with a loss of at most 5%
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car
We present DeepPicar, a low-cost deep neural network based autonomous car
platform. DeepPicar is a small scale replication of a real self-driving car
called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN),
which takes images from a front-facing camera as input and produces car
steering angles as output. DeepPicar uses the same network architecture---9
layers, 27 million connections and 250K parameters---and can drive itself in
real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using
DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end
deep learning based real-time control of autonomous vehicles. We also
systematically compare other contemporary embedded computing platforms using
the DeepPicar's CNN-based real-time control workload. We find that all tested
platforms, including the Pi 3, are capable of supporting the CNN-based
real-time control, from 20 Hz up to 100 Hz, depending on hardware platform.
However, we find that shared resource contention remains an important issue
that must be considered in applying CNN models on shared memory based embedded
computing platforms; we observe up to 11.6X execution time increase in the CNN
based control loop due to shared resource contention. To protect the CNN
workload, we also evaluate state-of-the-art cache partitioning and memory
bandwidth throttling techniques on the Pi 3. We find that cache partitioning is
ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201
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