601 research outputs found
Delay Measurements and Self Characterisation on FPGAs
This thesis examines new timing measurement methods for self delay characterisation of Field-Programmable Gate Arrays (FPGAs) components and delay measurement of complex circuits
on FPGAs. Two novel measurement techniques based on analysis of a circuit's output failure
rate and transition probability is proposed for accurate, precise and efficient measurement of
propagation delays. The transition probability based method is especially attractive, since
it requires no modifications in the circuit-under-test and requires little hardware resources,
making it an ideal method for physical delay analysis of FPGA circuits.
The relentless advancements in process technology has led to smaller and denser transistors
in integrated circuits. While FPGA users benefit from this in terms of increased hardware
resources for more complex designs, the actual productivity with FPGA in terms of timing
performance (operating frequency, latency and throughput) has lagged behind the potential
improvements from the improved technology due to delay variability in FPGA components
and the inaccuracy of timing models used in FPGA timing analysis. The ability to measure
delay of any arbitrary circuit on FPGA offers many opportunities for on-chip characterisation
and physical timing analysis, allowing delay variability to be accurately tracked and variation-aware optimisations to be developed, reducing the productivity gap observed in today's FPGA
designs.
The measurement techniques are developed into complete self measurement and characterisation platforms in this thesis, demonstrating their practical uses in actual FPGA hardware for
cross-chip delay characterisation and accurate delay measurement of both complex combinatorial and sequential circuits, further reinforcing their positions in solving the delay variability
problem in FPGAs
Innovative Techniques for Testing and Diagnosing SoCs
We rely upon the continued functioning of many electronic devices for our everyday welfare,
usually embedding integrated circuits that are becoming even cheaper and smaller
with improved features. Nowadays, microelectronics can integrate a working computer
with CPU, memories, and even GPUs on a single die, namely System-On-Chip (SoC).
SoCs are also employed on automotive safety-critical applications, but need to be tested
thoroughly to comply with reliability standards, in particular the ISO26262 functional
safety for road vehicles.
The goal of this PhD. thesis is to improve SoC reliability by proposing innovative
techniques for testing and diagnosing its internal modules: CPUs, memories, peripherals,
and GPUs. The proposed approaches in the sequence appearing in this thesis are described
as follows:
1. Embedded Memory Diagnosis: Memories are dense and complex circuits which
are susceptible to design and manufacturing errors. Hence, it is important to understand
the fault occurrence in the memory array. In practice, the logical and physical
array representation differs due to an optimized design which adds enhancements to
the device, namely scrambling. This part proposes an accurate memory diagnosis
by showing the efforts of a software tool able to analyze test results, unscramble
the memory array, map failing syndromes to cell locations, elaborate cumulative
analysis, and elaborate a final fault model hypothesis. Several SRAM memory failing
syndromes were analyzed as case studies gathered on an industrial automotive
32-bit SoC developed by STMicroelectronics. The tool displayed defects virtually,
and results were confirmed by real photos taken from a microscope.
2. Functional Test Pattern Generation: The key for a successful test is the pattern applied
to the device. They can be structural or functional; the former usually benefits
from embedded test modules targeting manufacturing errors and is only effective
before shipping the component to the client. The latter, on the other hand, can be
applied during mission minimally impacting on performance but is penalized due
to high generation time. However, functional test patterns may benefit for having
different goals in functional mission mode. Part III of this PhD thesis proposes
three different functional test pattern generation methods for CPU cores embedded
in SoCs, targeting different test purposes, described as follows:
a. Functional Stress Patterns: Are suitable for optimizing functional stress during
I
Operational-life Tests and Burn-in Screening for an optimal device reliability
characterization
b. Functional Power Hungry Patterns: Are suitable for determining functional
peak power for strictly limiting the power of structural patterns during manufacturing
tests, thus reducing premature device over-kill while delivering high test
coverage
c. Software-Based Self-Test Patterns: Combines the potentiality of structural patterns
with functional ones, allowing its execution periodically during mission.
In addition, an external hardware communicating with a devised SBST was proposed.
It helps increasing in 3% the fault coverage by testing critical Hardly
Functionally Testable Faults not covered by conventional SBST patterns.
An automatic functional test pattern generation exploiting an evolutionary algorithm
maximizing metrics related to stress, power, and fault coverage was employed
in the above-mentioned approaches to quickly generate the desired patterns. The
approaches were evaluated on two industrial cases developed by STMicroelectronics;
8051-based and a 32-bit Power Architecture SoCs. Results show that generation
time was reduced upto 75% in comparison to older methodologies while
increasing significantly the desired metrics.
3. Fault Injection in GPGPU: Fault injection mechanisms in semiconductor devices
are suitable for generating structural patterns, testing and activating mitigation techniques,
and validating robust hardware and software applications. GPGPUs are
known for fast parallel computation used in high performance computing and advanced
driver assistance where reliability is the key point. Moreover, GPGPU manufacturers
do not provide design description code due to content secrecy. Therefore,
commercial fault injectors using the GPGPU model is unfeasible, making radiation
tests the only resource available, but are costly. In the last part of this thesis, we
propose a software implemented fault injector able to inject bit-flip in memory elements
of a real GPGPU. It exploits a software debugger tool and combines the
C-CUDA grammar to wisely determine fault spots and apply bit-flip operations in
program variables. The goal is to validate robust parallel algorithms by studying
fault propagation or activating redundancy mechanisms they possibly embed. The
effectiveness of the tool was evaluated on two robust applications: redundant parallel
matrix multiplication and floating point Fast Fourier Transform
Multi-level simulation of nano-electronic digital circuits on GPUs
Simulation of circuits and faults is an essential part in design and test validation tasks of contemporary nano-electronic digital integrated CMOS circuits.
Shrinking technology processes with smaller feature sizes and strict performance and reliability requirements demand not only detailed validation of the functional properties of a design, but also accurate validation of non-functional aspects including the timing behavior. However, due to the rising complexity of the circuit behavior and the steady growth of the designs with respect to the transistor count, timing-accurate simulation of current designs requires a lot of computational effort which can only be handled by proper abstraction and a high degree of parallelization.
This work presents a simulation model for scalable and accurate timing simulation of digital circuits on data-parallel graphics processing unit (GPU) accelerators.
By providing compact modeling and data-structures as well as through exploiting multiple dimensions of parallelism, the simulation model enables not only fast and timing-accurate simulation at logic level, but also massively-parallel simulation with switch level accuracy.
The model facilitates extensions for fast and efficient fault simulation of small delay faults at logic level, as well as first-order parametric and parasitic faults at switch level.
With the parallelization on GPUs, detailed and scalable simulation is enabled that is applicable even to multi-million gate designs.
This way, comprehensive analyses of realistic timing-related faults in presence of process- and parameter variations are enabled for the first time.
Additional simulation efficiency is achieved by merging the presented methods in a unified simulation model, that allows to combine the unique advantages of the different levels of abstraction in a mixed-abstraction multi-level simulation flow to reach even higher speedups.
Experimental results show that the implemented parallel approach achieves unprecedented simulation throughput as well as high speedup compared to conventional timing simulators.
The underlying model scales for multi-million gate designs and gives detailed insights into the timing behavior of digital CMOS circuits, thereby enabling large-scale applications to aid even highly complex design and test validation tasks
Soft Error Resistant Design of the AES Cipher Using SRAM-based FPGA
This thesis presents a new architecture for the reliable implementation of the symmetric-key algorithm Advanced Encryption Standard (AES) in Field Programmable Gate Arrays (FPGAs). Since FPGAs are prone to soft errors caused by radiation, and AES is highly sensitive to errors, reliable architectures are of significant concern. Energetic particles hitting a device can flip bits in FPGA SRAM cells controlling all aspects of the implementation. Unlike previous research, heterogeneous error detection techniques based on properties of the circuit and functionality are used to provide adequate reliability at the lowest possible cost. The use of dual ported block memory for SubBytes, duplication for the control circuitry, and a new enhanced parity technique for MixColumns is proposed. Previous parity techniques cover single errors in datapath registers, however, soft errors can occur in the control circuitry as well as in SRAM cells forming the combinational logic and routing. In this research, propagation of single errors is investigated in the routed netlist. Weaknesses of the previous parity techniques are identified. Architectural redesign at the register-transfer level is introduced to resolve undetected single errors in both the routing and the combinational logic.
Reliability of the AES implementation is not only a critical issue in large scale FPGA-based systems but also at both higher altitudes and in space applications where there are a larger number of energetic particles. Thus, this research is important for providing efficient soft error resistant design in many current and future secure applications
Hardware Acceleration of Electronic Design Automation Algorithms
With the advances in very large scale integration (VLSI) technology, hardware is going
parallel. Software, which was traditionally designed to execute on single core microprocessors,
now faces the tough challenge of taking advantage of this parallelism, made available
by the scaling of hardware. The work presented in this dissertation studies the acceleration
of electronic design automation (EDA) software on several hardware platforms such
as custom integrated circuits (ICs), field programmable gate arrays (FPGAs) and graphics
processors. This dissertation concentrates on a subset of EDA algorithms which are heavily
used in the VLSI design flow, and also have varying degrees of inherent parallelism
in them. In particular, Boolean satisfiability, Monte Carlo based statistical static timing
analysis, circuit simulation, fault simulation and fault table generation are explored. The
architectural and performance tradeoffs of implementing the above applications on these
alternative platforms (in comparison to their implementation on a single core microprocessor)
are studied. In addition, this dissertation also presents an automated approach to
accelerate uniprocessor code using a graphics processing unit (GPU). The key idea is to
partition the software application into kernels in an automated fashion, such that multiple
instances of these kernels, when executed in parallel on the GPU, can maximally benefit
from the GPU?s hardware resources.
The work presented in this dissertation demonstrates that several EDA algorithms can
be successfully rearchitected to maximally harness their performance on alternative platforms
such as custom designed ICs, FPGAs and graphic processors, and obtain speedups upto 800X. The approaches in this dissertation collectively aim to contribute towards enabling
the computer aided design (CAD) community to accelerate EDA algorithms on arbitrary
hardware platforms
Intelligent Circuits and Systems
ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering
Modeling and Simulation in Engineering
The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering
Advances in Grid Computing
This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems
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