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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%
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