175 research outputs found
VLSI implementation of a massively parallel wavelet based zerotree coder for the intelligent pixel array
In the span of a few years, mobile multimedia communication has rapidly become a significant area of research and development constantly challenging boundaries on a variety of technologic fronts. Mobile video communications in particular encompasses a number of technical hurdles that generally steer technological advancements towards devices that are low in complexity, low in power usage yet perform the given task efficiently. Devices of this nature have been made available through the use of massively parallel processing arrays such as the Intelligent Pixel Processing Array. The Intelligent Pixel Processing array is a novel concept that integrates a parallel image capture mechanism, a parallel processing component and a parallel display component into a single chip solution geared toward mobile communications environments, be it a PDA based system or the video communicator wristwatch portrayed in Dick Tracy episodes. This thesis details work performed to provide an efficient, low power, low complexity solution surrounding the massively parallel implementation of a zerotree entropy codec for the Intelligent Pixel Array
Primitives and design of the intelligent pixel multimedia communicator
Communication systems arc an ever more essential component of our modern global society. Mobile communications systems are still in a state of rapid advancement and growth. Technology is constantly evolving at a rapid pace in ever more diverse areas and the emerging mobile multimedia based communication systems offer new challenges for both current and future technologies. To realise the full potential of mobile multimedia communication systems there is a need to explore new options to solve some of the fundamental problems facing the technology. In particular, the complexity of such a system within an infrastructure framework that is inherently limited by its power sources and has very restricted transmission bandwidth demands new methodologies and approaches
Benchmarking Edge Computing Devices for Grape Bunches and Trunks Detection using Accelerated Object Detection Single Shot MultiBox Deep Learning Models
Purpose: Visual perception enables robots to perceive the environment. Visual
data is processed using computer vision algorithms that are usually
time-expensive and require powerful devices to process the visual data in
real-time, which is unfeasible for open-field robots with limited energy. This
work benchmarks the performance of different heterogeneous platforms for object
detection in real-time. This research benchmarks three architectures: embedded
GPU -- Graphical Processing Units (such as NVIDIA Jetson Nano 2 GB and 4 GB,
and NVIDIA Jetson TX2), TPU -- Tensor Processing Unit (such as Coral Dev Board
TPU), and DPU -- Deep Learning Processor Unit (such as in AMD-Xilinx ZCU104
Development Board, and AMD-Xilinx Kria KV260 Starter Kit). Method: The authors
used the RetinaNet ResNet-50 fine-tuned using the natural VineSet dataset.
After the trained model was converted and compiled for target-specific hardware
formats to improve the execution efficiency. Conclusions and Results: The
platforms were assessed in terms of performance of the evaluation metrics and
efficiency (time of inference). Graphical Processing Units (GPUs) were the
slowest devices, running at 3 FPS to 5 FPS, and Field Programmable Gate Arrays
(FPGAs) were the fastest devices, running at 14 FPS to 25 FPS. The efficiency
of the Tensor Processing Unit (TPU) is irrelevant and similar to NVIDIA Jetson
TX2. TPU and GPU are the most power-efficient, consuming about 5W. The
performance differences, in the evaluation metrics, across devices are
irrelevant and have an F1 of about 70 % and mean Average Precision (mAP) of
about 60 %
Recent Advances in Embedded Computing, Intelligence and Applications
The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems
Efficient reconfigurable architectures for 3D medical image compression
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities,
such as magnetic resonance imaging (MRI), computed tomography (CT), positron
emission tomography (PET), and ultrasound (US) have generated a massive amount
of volumetric data. These have provided an impetus to the development of other
applications, in particular telemedicine and teleradiology. In these fields, medical
image compression is important since both efficient storage and transmission of data
through high-bandwidth digital communication lines are of crucial importance.
Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow
for quick upgradeability with real-time applications. Moreover, in order to obtain
efficient solutions for large medical volumes data, an efficient implementation of
these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system
building block in the construction of high-performance systems at an economical price.
Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent
advantages such as massive parallelism capabilities, multimillion gate counts, and
special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are
optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits
promising results in reducing Gaussian white noise in medical images. In terms of
hardware implementation, promising trade-offs on maximum frequency, throughput
and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC)
has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete
wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that
3-D IT demonstrates better computational complexity than the 3-D DWT, whilst
the 3-D DWT with LS exhibits a lossless compression that is significantly useful for
medical image compression. Additionally, an architecture of CAVLC that is capable
of compressing high-definition (HD) images in real-time without any buffer between
the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the
slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.Ministry of Higher Education Malaysia (MOHE),
Universiti Tun Hussein Onn Malaysia (UTHM) and the British Counci
Agile Reactive Navigation for A Non-Holonomic Mobile Robot Using A Pixel Processor Array
This paper presents an agile reactive navigation strategy for driving a
non-holonomic ground vehicle around a preset course of gates in a cluttered
environment using a low-cost processor array sensor. This enables machine
vision tasks to be performed directly upon the sensor's image plane, rather
than using a separate general-purpose computer. We demonstrate a small ground
vehicle running through or avoiding multiple gates at high speed using minimal
computational resources. To achieve this, target tracking algorithms are
developed for the Pixel Processing Array and captured images are then processed
directly on the vision sensor acquiring target information for controlling the
ground vehicle. The algorithm can run at up to 2000 fps outdoors and 200fps at
indoor illumination levels. Conducting image processing at the sensor level
avoids the bottleneck of image transfer encountered in conventional sensors.
The real-time performance of on-board image processing and robustness is
validated through experiments. Experimental results demonstrate that the
algorithm's ability to enable a ground vehicle to navigate at an average speed
of 2.20 m/s for passing through multiple gates and 3.88 m/s for a 'slalom' task
in an environment featuring significant visual clutter.Comment: 7 page
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