23 research outputs found
Efficient runtime placement management for high performance and reliability in COTS FPGAs
Designing high-performance, fault-tolerant multisensory electronic systems for
hostile environments such as nuclear plants and outer space within the constraints of
cost, power and flexibility is challenging. Issues such as ionizing radiation, extreme
temperature and ageing can lead to faults in the electronics of these systems. In
addition, the remote nature of these environments demands a level of flexibility and
autonomy in their operations. The standard practice of using specially hardened
electronic devices for such systems is not only very expensive but also has limited
flexibility.
This thesis proposes novel techniques that promote the use of Commercial Off-The-
Shelf (COTS) reconfigurable devices to meet the challenges of high-performance
systems for hostile environments. Reconfigurable hardware such as Field
Programmable Gate Arrays (FPGA) have a unique combination of flexibility and
high performance. The flexibility offered through features such as dynamic partial
reconfiguration (DPR) can be harnessed not only to achieve cost-effective designs as
a smaller area can be used to execute multiple tasks, but also to improve the
reliability of a system as a circuit on one portion of the device can be physically
relocated to another portion in the case of fault occurrence. However, to harness
these potentials for high performance and reliability in a cost-effective manner, novel
runtime management tools are required. Most runtime support tools for
reconfigurable devices are based on ideal models which do not adequately consider
the limitations of realistic FPGAs, in particular modern FPGAs which are
increasingly heterogeneous. Specifically, these tools lack efficient mechanisms for
ensuring a high utilization of FPGA resources, including the FPGA area and the
configuration port and clocking resources, in a reliable manner.
To ensure high utilization of reconfigurable device area, placement management is a
key aspect of these tools. This thesis presents novel techniques for the management
of hardware task placement on COTS reconfigurable devices for high performance
and reliability. To this end, it addresses design-time issues that affect efficient
hardware task placement, with a focus on reliability. It also presents techniques to
maximize the utilization of the FPGA area in runtime, including techniques to
minimize fragmentation. Fragmentation leads to the creation of unusable areas due to
dynamic placement of tasks and the heterogeneity of the resources on the chip.
Moreover, this thesis also presents an efficient task reuse mechanism to improve the
availability of the internal configuration infrastructure of the FPGA for critical
responsibilities like error mitigation. The task reuse scheme, unlike previous
approaches, also improves the utilization of the chip area by offering
defragmentation.
Task relocation, which involves changing the physical location of circuits is a
technique for error mitigation and high performance. Hence, this thesis also provides
a functionality-based relocation mechanism for improving the number of locations to
which tasks can be relocated on heterogeneous FPGAs. As tasks are relocated, clock
networks need to be routed to them. As such, a reliability-aware technique of clock
network routing to tasks after placement is also proposed.
Finally, this thesis offers a prototype implementation and characterization of a
placement management system (PMS) which is an integration of the aforementioned
techniques. The performance of most of the proposed techniques are tested using
data processing tasks of a NASA JPL spectrometer application. The results show that
the proposed techniques have potentials to improve the reliability and performance of
applications in hostile environment compared to state-of-the-art techniques. The task
optimization technique presented leads to better capacity to circumvent permanent
faults on COTS FPGAs compared to state-of-the-art approaches (48.6% more errors
were circumvented for the JPL spectrometer application). The proposed task reuse
scheme leads to approximately 29% saving in the amount of configuration time. This
frees up the internal configuration interface for more error mitigation operations. In
addition, the proposed PMS has a worst-case latency of less than 50% of that of state-of-
the-art runtime placement systems, while maintaining the same level of placement
quality and resource overhead
Design of a silicon cochlea system with biologically faithful response
This paper presents the design and simulation results of a silicon cochlea system that has closely similar behavior as the real cochlea. A cochlea filter-bank based on the improved three-stage filter cascade structure is used to model the frequency decomposition function of the basilar membrane; a filter tuning block is designed to model the adaptive response of the cochlea; besides, an asynchronous event-triggered spike codec is employed as the system interface with bank-end spiking neural networks. As shown in the simulation results, the system has biologically faithful frequency response, impulse response, and active adaptation behavior; also the system outputs multiple
band-pass channels of spikes from which the original sound input can be recovered. The proposed silicon cochlea is feasible for analog VLSI implementation so that it not only emulates the way that sounds are preprocessed in human ears but also is able match the compact physical size of a real cochlea
Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography
Chemical Species Tomography (CST) has been widely applied for imaging of
critical gas-phase parameters in industrial processes. To acquire high-fidelity
images, CST is typically implemented by line-of-sight Wavelength Modulation
Spectroscopy (WMS) measurements from multiple laser beams. The modulated
transmission signal on each laser beam needs to be a) digitised by a high-speed
analogue-to-digital converter (ADC); b) demodulated by a digital lock-in (DLI)
module; and c) transferred to high-level processor for image reconstruction.
Although a fully parallel data acquisition (DAQ) and signal processing system
can achieve these functionalities with maximised temporal response, it leads to
a highly complex, expensive and power-consuming instrumentation system with
high potential for inconsistency between the sampled beams due to the
electronics alone. In addition, the huge amount of spectral data sampled in
parallel significantly burdens the communication process in industrial
applications where in situ signal digitisation is distanced from the high-level
data processing. To address these issues, a quasi-parallel sensing technique
and electronic circuits were developed for industrial CST, in which the
digitisation and demodulation of the multi-beam transmission signals are
multiplexed over the high-frequency modulation within a wavelength scan. Our
development not only maintains the temporal response of the fully parallel
sensing scheme, but also facilitates the cost-effective implementation of
industrial CST with very low complexity and reduced load on data transfer. The
proposed technique is analytically proven, numerically examined by
noise-contaminated CST simulations, and experimentally validated using a
lab-scale CST system with 32 laser beams.Comment: Submitted to IEEE Transactions on Industrial Electronic
Hierarchical temperature imaging using pseudoinversed convolutional neural network aided TDLAS tomography
As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption
Spectroscopy (TDLAS) tomography has been widely used for imaging of
two-dimensional temperature distributions in reactive flows. Compared with the
computational tomographic algorithms, Convolutional Neural Networks (CNNs) have
been proofed to be more robust and accurate for image reconstruction,
particularly in case of limited access of laser beams in the Region of Interest
(RoI). In practice, flame in the RoI that requires to be reconstructed with
good spatial resolution is commonly surrounded by low-temperature background.
Although the background is not of high interest, spectroscopic absorption still
exists due to heat dissipation and gas convection. Therefore, we propose a
Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses
efficiently the training and learning resources for temperature imaging in the
RoI with good spatial resolution, and (b) reconstructs the less spatially
resolved background temperature by adequately addressing the integrity of the
spectroscopic absorption model. In comparison with the traditional CNN, the
newly introduced pseudo inversion of the RoI sensitivity matrix is more
penetrating for revealing the inherent correlation between the projection data
and the RoI to be reconstructed, thus prioritising the temperature imaging in
the RoI with high accuracy and high computational efficiency. In this paper,
the proposed algorithm was validated by both numerical simulation and lab-scale
experiment, indicating good agreement between the phantoms and the
high-fidelity reconstructions.Comment: Submitted to IEEE Transactions on Instrumentation and Measuremen
Relative Entropy Regularised TDLAS Tomography for Robust Temperature Imaging
Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been
widely used for in situ combustion diagnostics, yielding images of both species
concentration and temperature. The temperature image is generally obtained from
the reconstructed absorbance distributions for two spectral transitions, i.e.
two-line thermometry. However, the inherently ill-posed nature of tomographic
data inversion leads to noise in each of the reconstructed absorbance
distributions. These noise effects propagate into the absorbance ratio and
generate artefacts in the retrieved temperature image. To address this problem,
we have developed a novel algorithm, which we call Relative Entropy Tomographic
RecOnstruction (RETRO), for TDLAS tomography. A relative entropy regularisation
is introduced for high-fidelity temperature image retrieval from jointly
reconstructed two-line absorbance distributions. We have carried out numerical
simulations and proof-of-concept experiments to validate the proposed
algorithm. Compared with the well-established Simultaneous Algebraic
Reconstruction Technique (SART), the RETRO algorithm significantly improves the
quality of the tomographic temperature images, exhibiting excellent robustness
against TDLAS tomographic measurement noise. RETRO offers great potential for
industrial field applications of TDLAS tomography, where it is common for
measurements to be performed in very harsh environments.Comment: Preprint submitted to IEEE Transactions on Instrumentation and
Measuremen