103 research outputs found
Reconfigurable architectures for the next generation of mobile device telecommunications systems
Mobile devices have become a dominant tool in our daily lives. Business and
personal usage has escalated tremendously since the emergence of smartphones
and tablets. The combination of powerful processing in mobile devices, such as
smartphones and the Internet, have established a new era for communications
systems. This has put further pressure on the performance and efficiency of
telecommunications systems in delivering the aspirations of users. Mobile device
users no longer want devices that merely perform phone calls and messaging.
Rather, they look for further interactive applications such as video streaming,
navigation and real time social interaction. Such applications require a new set of
hardware and standards. The WiFi (IEEE 802.11) standard has been at the forefront
of reliable and high-speed internet access telecommunications. This is due to its
high signal quality (quality of service) and speed (throughput). However, its limited
availability and short range highlights the need for further protocols, in particular
when far away from access points or base stations. This led to the emergence of 3G
followed by 4G and the upcoming 5G standard that, if fully realised, will provide
another dimension in âanywhere, anytime internet connectivity.â On the other
hand, the WiMAX (IEEE 802.16) standard promises to exceed the WiFi signal
coverage range. The coverage range could be extended to kilometres at least with a
better or similar WiFi signal level.
This thesis considers a dynamically reconfigurable architecture that is capable of
processing various modules within telecommunications systems. Forward error
correction, coder and navigation modules are deployed in a unified low power
communication platform. These modules have been selected since they are among
those with the highest demand in terms of processing power, strict processing time
or throughput. The modules are mainly realised within WiFi and WiMAX systems
in addition to global positioning systems (GPS). The idea behind the selection of
these modules is to investigate the possibility of designing an architecture capable
of processing various systems and dynamically reconfiguring between them. The
GPS system is a power-hungry application and, at the same time, it is not needed
all of the time. Hence, one key idea presented in this thesis is to effectively exploit
the dynamic reconfiguration capability so as to reconfigure the architecture (GPS)
when it is not needed in order to process another needed application or function
such as WiFi or WiMAX. This will allow lower energy consumption and the
optimum usage of the hardware available on the device.
This work investigates the major current coarse-grain reconfigurable architectures.
A novel multi-rate convolution encoder is then designed and realised as a
reconfigurable fabric. This demonstrates the ability to adapt the algorithms
involved to meet various requirements. A throughput of between 200 and 800
Mbps has been achieved for the rates 1/2 to 7/8, which is a great achievement for
the proposed novel architecture. A reconfigurable interleaver is designed as a
standalone fabric and on a dynamically reconfigurable processor. High throughputs
exceeding 90 Mbps are achieved for the various supported block sizes. The Reed
Solomon coder is the next challenging system to be designed into a dynamically
reconfigurable processor. A novel Galois Field multiplier is designed and
integrated into the developed Reed Solomon reconfigurable processor. As a result
of this work, throughputs of 200Mbps and 93Mbps respectively for RS encoding
and decoding are achieved. A GPS correlation module is also investigated in this
work. This is the main part of the GPS receiver responsible for continuously
tracking GPS satellites and extracting messages from them. The challenging aspect
of this part is its real-time nature and the associated critical time constraints. This
work resulted in a novel dynamically reconfigurable multi-channel GPS correlator
with up to 72 simultaneous channels.
This work is a contribution towards a global unified processing platform that is
capable of processing communication-related operations efficiently and
dynamically with minimum energy consumption
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Efficient FPGA implementation and power modelling of image and signal processing IP cores
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Field Programmable Gate Arrays (FPGAs) are the technology of choice in a number ofimage
and signal processing application areas such as consumer electronics, instrumentation,
medical data processing and avionics due to their reasonable energy consumption, high performance, security, low design-turnaround time and reconfigurability. Low power FPGA
devices are also emerging as competitive solutions for mobile and thermally constrained platforms. Most computationally intensive image and signal processing algorithms also consume a lot of power leading to a number of issues including reduced mobility, reliability concerns and increased design cost among others. Power dissipation has become one of the most important challenges, particularly for FPGAs. Addressing this problem requires optimisation and awareness at all levels in the design flow. The key achievements of the
work presented in this thesis are summarised here. Behavioural level optimisation strategies have been used for implementing matrix product and inner product through the use of mathematical techniques such as Distributed Arithmetic (DA) and its variations including offset binary coding, sparse factorisation and novel vector level transformations. Applications to test the impact of these algorithmic and arithmetic transformations include the fast Hadamard/Walsh transforms and Gaussian mixture models. Complete design space exploration has been performed on these cores, and where appropriate, they have been shown to clearly outperform comparable existing implementations. At the architectural level, strategies such as parallelism, pipelining and systolisation have been successfully applied for the design and optimisation of a number of
cores including colour space conversion, finite Radon transform, finite ridgelet transform and circular convolution. A pioneering study into the influence of supply voltage scaling for FPGA based designs, used in conjunction with performance enhancing strategies such as parallelism and pipelining has been performed. Initial results are very promising and indicated significant potential for future research in this area.
A key contribution of this work includes the development of a novel high level power macromodelling technique for design space exploration and characterisation of custom IP cores for FPGAs, called Functional Level Power Analysis and Modelling (FLPAM). FLPAM
is scalable, platform independent and compares favourably with existing approaches. A hybrid, top-down design flow paradigm integrating FLPAM with commercially available design tools for systematic optimisation of IP cores has also been developed
A Dynamically Reconfigurable Parallel Processing Framework with Application to High-Performance Video Processing
Digital video processing demands have and will continue to grow at unprecedented rates. Growth comes from ever increasing volume of data, demand for higher resolution, higher frame rates, and the need for high capacity communications. Moreover, economic realities force continued reductions in size, weight and power requirements. The ever-changing needs and complexities associated with effective video processing systems leads to the consideration of dynamically reconfigurable systems. The goal of this dissertation research was to develop and demonstrate the viability of integrated parallel processing system that effectively and efficiently apply pre-optimized hardware cores for processing video streamed data. Digital video is decomposed into packets which are then distributed over a group of parallel video processing cores. Real time processing requires an effective task scheduler that distributes video packets efficiently to any of the reconfigurable distributed processing nodes across the framework, with the nodes running on FPGA reconfigurable logic in an inherently Virtual\u27 mode. The developed framework, coupled with the use of hardware techniques for dynamic processing optimization achieves an optimal cost/power/performance realization for video processing applications. The system is evaluated by testing processor utilization relative to I/O bandwidth and algorithm latency using a separable 2-D FIR filtering system, and a dynamic pixel processor. For these applications, the system can achieve performance of hundreds of 640x480 video frames per second across an eight lane Gen I PCIe bus. Overall, optimal performance is achieved in the sense that video data is processed at the maximum possible rate that can be streamed through the processing cores. This performance, coupled with inherent ability to dynamically add new algorithms to the described dynamically reconfigurable distributed processing framework, creates new opportunities for realizable and economic hardware virtualization.\u2
High performance reconfigurable architectures for biological sequence alignment
Bioinformatics and computational biology (BCB) is a rapidly developing
multidisciplinary field which encompasses a wide range of domains, including genomic
sequence alignments. It is a fundamental tool in molecular biology in searching for
homology between sequences. Sequence alignments are currently gaining close attention due
to their great impact on the quality aspects of life such as facilitating early disease diagnosis,
identifying the characteristics of a newly discovered sequence, and drug engineering. With
the vast growth of genomic data, searching for a sequence homology over huge databases
(often measured in gigabytes) is unable to produce results within a realistic time, hence the
need for acceleration. Since the exponential increase of biological databases as a result of the
human genome project (HGP), supercomputers and other parallel architectures such as the
special purpose Very Large Scale Integration (VLSI) chip, Graphic Processing Unit (GPUs)
and Field Programmable Gate Arrays (FPGAs) have become popular acceleration platforms.
Nevertheless, there are always trade-off between area, speed, power, cost, development time
and reusability when selecting an acceleration platform. FPGAs generally offer more
flexibility, higher performance and lower overheads. However, they suffer from a relatively
low level programming model as compared with off-the-shelf microprocessors such as
standard microprocessors and GPUs. Due to the aforementioned limitations, the need has
arisen for optimized FPGA core implementations which are crucial for this technology to
become viable in high performance computing (HPC).
This research proposes the use of state-of-the-art reprogrammable system-on-chip
technology on FPGAs to accelerate three widely-used sequence alignment algorithms; the
Smith-Waterman with affine gap penalty algorithm, the profile hidden Markov model
(HMM) algorithm and the Basic Local Alignment Search Tool (BLAST) algorithm. The
three novel aspects of this research are firstly that the algorithms are designed and
implemented in hardware, with each core achieving the highest performance compared to the
state-of-the-art. Secondly, an efficient scheduling strategy based on the double buffering
technique is adopted into the hardware architectures. Here, when the alignment matrix
computation task is overlapped with the PE configuration in a folded systolic array, the
overall throughput of the core is significantly increased. This is due to the bound PE
configuration time and the parallel PE configuration approach irrespective of the number of
PEs in a systolic array. In addition, the use of only two configuration elements in the PE optimizes hardware resources and enables the scalability of PE systolic arrays without
relying on restricted onboard memory resources. Finally, a new performance metric is
devised, which facilitates the effective comparison of design performance between different
FPGA devices and families. The normalized performance indicator (speed-up per area per
process technology) takes out advantages of the area and lithography technology of any
FPGA resulting in fairer comparisons.
The cores have been designed using Verilog HDL and prototyped on the Alpha Data
ADM-XRC-5LX card with the Virtex-5 XC5VLX110-3FF1153 FPGA. The implementation
results show that the proposed architectures achieved giga cell updates per second (GCUPS)
performances of 26.8, 29.5 and 24.2 respectively for the acceleration of the Smith-Waterman
with affine gap penalty algorithm, the profile HMM algorithm and the BLAST algorithm. In
terms of speed-up improvements, comparisons were made on performance of the designed
cores against their corresponding software and the reported FPGA implementations. In the
case of comparison with equivalent software execution, acceleration of the optimal
alignment algorithm in hardware yielded an average speed-up of 269x as compared to the
SSEARCH 35 software. For the profile HMM-based sequence alignment, the designed core
achieved speed-up of 103x and 8.3x against the HMMER 2.0 and the latest version of
HMMER (version 3.0) respectively. On the other hand, the implementation of the gapped
BLAST with the two-hit method in hardware achieved a greater than tenfold speed-up
compared to the latest NCBI BLAST software. In terms of comparison against other reported
FPGA implementations, the proposed normalized performance indicator was used to
evaluate the designed architectures fairly. The results showed that the first architecture
achieved more than 50 percent improvement, while acceleration of the profile HMM
sequence alignment in hardware gained a normalized speed-up of 1.34. In the case of the
gapped BLAST with the two-hit method, the designed core achieved 11x speed-up after
taking out advantages of the Virtex-5 FPGA. In addition, further analysis was conducted in
terms of cost and power performances; it was noted that, the core achieved 0.46 MCUPS per
dollar spent and 958.1 MCUPS per watt. This shows that FPGAs can be an attractive
platform for high performance computation with advantages of smaller area footprint as well
as represent economic âgreenâ solution compared to the other acceleration platforms. Higher
throughput can be achieved by redeploying the cores on newer, bigger and faster FPGAs
with minimal design effort
High performance communication on reconfigurable clusters
High Performance Computing (HPC) has matured to where it is an essential third pillar, along with theory and experiment, in most domains of science and engineering. Communication latency is a key factor that is limiting the performance of HPC, but can be addressed by integrating communication into accelerators. This integration allows accelerators to communicate with each other without CPU interactions, and even bypassing the network stack. Field Programmable Gate Arrays (FPGAs) are the accelerators that currently best integrate communication with computation. The large number of Multi-gigabit Transceivers (MGTs) on most high-end FPGAs can provide high-bandwidth and low-latency inter-FPGA connections. Additionally, the reconfigurable FPGA fabric enables tight coupling between computation kernel and network interface.
Our thesis is that an application-aware communication infrastructure for a multi-FPGA system makes substantial progress in solving the HPC communication bottleneck. This dissertation aims to provide an application-aware solution for communication infrastructure for FPGA-centric clusters. Specifically, our solution demonstrates application-awareness across multiple levels in the network stack, including low-level link protocols, router microarchitectures, routing algorithms, and applications.
We start by investigating the low-level link protocol and the impact of its latency variance on performance. Our results demonstrate that, although some link jitter is always present, we can still assume near-synchronous communication on an FPGA-cluster. This provides the necessary condition for statically-scheduled routing. We then propose two novel router microarchitectures for two different kinds of workloads: a wormhole Virtual Channel (VC)-based router for workloads with dynamic communication, and a statically-scheduled Virtual Output Queueing (VOQ)-based router for workloads with static communication. For the first (VC-based) router, we propose a framework that generates application-aware router configurations. Our results show that, by adding application-awareness into router configuration, the network performance of FPGA clusters can be substantially improved. For the second (VOQ-based) router, we propose a novel offline collective routing algorithm. This shows a significant advantage over a state-of-the-art collective routing algorithm.
We apply our communication infrastructure to a critical strong-scaling HPC kernel, the 3D FFT. The experimental results demonstrate that the performance of our design is faster than that on CPUs and GPUs by at least one order of magnitude (achieving strong scaling for the target applications). Surprisingly, the FPGA cluster performance is similar to that of an ASIC-cluster. We also implement the 3D FFT on another multi-FPGA platform: the Microsoft Catapult II cloud. Its performance is also comparable or superior to CPU and GPU HPC clusters. The second application we investigate is Molecular Dynamics Simulation (MD). We model MD on both FPGA clouds and clusters. We find that combining processing and general communication in the same device leads to extremely promising performance and the prospect of MD simulations well into the us/day range with a commodity cloud
Dynamically and partially reconfigurable hardware architectures for high performance microarray bioinformatics data analysis
The field of Bioinformatics and Computational Biology (BCB) is a multidisciplinary field
that has emerged due to the computational demands of current state-of-the-art biotechnology.
BCB deals with the storage, organization, retrieval, and analysis of biological datasets,
which have grown in size and complexity in recent years especially after the completion of
the human genome project. The advent of Microarray technology in the 1990s has resulted in
the new concept of high throughput experiment, which is a biotechnology that measures the
gene expression profiles of thousands of genes simultaneously. As such, Microarray requires
high computational power to extract the biological relevance from its high dimensional data.
Current general purpose processors (GPPs) has been unable to keep-up with the increasing
computational demands of Microarrays and reached a limit in terms of clock speed.
Consequently, Field Programmable Gate Arrays (FPGAs) have been proposed as a low
power viable solution to overcome the computational limitations of GPPs and other methods.
The research presented in this thesis harnesses current state-of-the-art FPGAs and tools to
accelerate some of the most widely used data mining methods used for the analysis of
Microarray data in an effort to investigate the viability of the technology as an efficient, low
power, and economic solution for the analysis of Microarray data. Three widely used
methods have been selected for the FPGA implementations: one is the un-supervised Kmeans
clustering algorithm, while the other two are supervised classification methods,
namely, the K-Nearest Neighbour (K-NN) and Support Vector Machines (SVM). These
methods are thought to benefit from parallel implementation. This thesis presents detailed
designs and implementations of these three BCB applications on FPGA captured in Verilog
HDL, whose performance are compared with equivalent implementations running on GPPs.
In addition to acceleration, the benefits of current dynamic partial reconfiguration (DPR)
capability of modern Xilinxâ FPGAs are investigated with reference to the aforementioned
data mining methods.
Implementing K-means clustering on FPGA using non-DPR design flow has
outperformed equivalent implementations in GPP and GPU in terms of speed-up by two
orders and one order of magnitude, respectively; while being eight times more power
efficient than GPP and four times more than a GPU implementation. As for the energy
efficiency, the FPGA implementation was 615 times more energy efficient than GPPs, and 31 times more than GPUs. Over and above, the FPGA implementation outperformed the
GPP and GPU implementations in terms of speed-up as the dimensionality of the Microarray
data increases. Additionally, the DPR implementations of the K-means clustering have
shown speed-up in partial reconfiguration time of ~5x and 17x over full chip reconfiguration
for single-core and eight-core implementations, respectively.
Two architectures of the K-NN classifier have been implemented on FPGA, namely, A1
and A2. The K-NN implementation based on A1 architecture achieved a speed-up of ~76x
over an equivalent GPP implementation whereas the A2 architecture achieved ~68x speedup.
Furthermore, the FPGA implementation outperformed the equivalent GPP
implementation when the dimensionality of data was increased. In addition, The DPR
implementations of the K-NN classifier have achieved speed-ups in reconfiguration time
between ~4x to 10x over full chip reconfiguration when reconfiguring portion of the
classifier or the complete classifier.
Similar to K-NN, two architectures of the SVM classifier were implemented on FPGA
whereby the former outperformed an equivalent GPP implementation by ~61x and the latter
by ~49x. As for the DPR implementation of the SVM classifier, it has shown a speed-up of
~8x in reconfiguration time when reconfiguring the complete core or when exchanging it
with a K-NN core forming a multi-classifier.
The aforementioned implementations clearly show FPGAs to be an efficacious, efficient
and economic solution for bioinformatics Microarrays data analysis
Dynamically reconfigurable architecture for embedded computer vision systems
The objective of this research work is to design, develop and implement a new architecture which integrates on the same chip all the processing levels of a complete Computer Vision system, so that the execution is efficient without compromising the power consumption while keeping a reduced cost. For this purpose, an analysis and classification of different mathematical operations and algorithms commonly used in Computer Vision are carried out, as well as a in-depth review of the image processing capabilities of current-generation hardware devices. This permits to determine the requirements and the key aspects for an efficient architecture. A representative set of algorithms is employed as benchmark to evaluate the proposed architecture, which is implemented on an FPGA-based system-on-chip. Finally, the prototype is compared to other related approaches in order to determine its advantages and weaknesses
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
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