790 research outputs found
Optimizing Scrubbing by Netlist Analysis for FPGA Configuration Bit Classification and Floorplanning
Existing scrubbing techniques for SEU mitigation on FPGAs do not guarantee an
error-free operation after SEU recovering if the affected configuration bits do
belong to feedback loops of the implemented circuits. In this paper, we a)
provide a netlist-based circuit analysis technique to distinguish so-called
critical configuration bits from essential bits in order to identify
configuration bits which will need also state-restoring actions after a
recovered SEU and which not. Furthermore, b) an alternative classification
approach using fault injection is developed in order to compare both
classification techniques. Moreover, c) we will propose a floorplanning
approach for reducing the effective number of scrubbed frames and d),
experimental results will give evidence that our optimization methodology not
only allows to detect errors earlier but also to minimize the
Mean-Time-To-Repair (MTTR) of a circuit considerably. In particular, we show
that by using our approach, the MTTR for datapath-intensive circuits can be
reduced by up to 48.5% in comparison to standard approaches
Developments and experimental evaluation of partitioning algorithms for adaptive computing systems
Multi-FPGA systems offer the potential to deliver higher performance solutions than traditional computers for some low-level computing tasks. This requires a flexible hardware substrate and an automated mapping system. CHAMPION is an automated mapping system for implementing image processing applications in multi-FPGA systems under development at the University of Tennessee. CHAMPION will map applications in the Khoros Cantata graphical programming environment to hardware. The work described in this dissertation involves the automation of the CHAMPION backend design flow, which includes the partitioning problem, netlist to structural VHDL conversion, synthesis and placement and routing, and host code generation. The primary goal is to investigate the development and evaluation of three different k-way partitioning approaches. In the first and the second approaches, we discuss the development and implementation of two existing algorithms. The first approach is a hierarchical partitioning method based on topological ordering (HP). The second approach is a recursive algorithm based on the Fiduccia and Mattheyses bipartitioning heuristic (RP). We extend these algorithms to handle the multiple constraints imposed by adaptive computing systems. We also introduce a new recursive partitioning method based on topological ordering and levelization (RPL). In addition to handling the partitioning constraints, the new approach efficiently addresses the problem of minimizing the number of FPGAs used and the amount of computation, thereby overcoming some of the weaknesses of the HP and RP algorithms
Automatic mapping of graphical programming applications to microelectronic technologies
Adaptive computing systems (ACSs) and application-specific integrated circuits (ASICs) can serve as flexible hardware accelerators for applications in domains such as image processing and digital signal processing. However, the mapping of applications onto ACSs and ASICs using the traditional methods can take months for a hardware engineer to develop and debug. In this dissertation, a new approach for automatic mapping of software applications onto ACSs and ASICs has been developed, implemented and validated. This dissertation presents the design flow of the software environment called CHAMPION, which is being developed at the University of Tennessee. This environment permits high-level design entry using the Cantata graphical programming software fromKRI. Using Cantata as the design entry, CHAMPION hides from the user the low-level details of the hardware architecture and the finer issues of application mapping onto the hardware. Validation of the CHAMPION environment was performed using multiple applications of moderate complexity. In one case, theapplication mapping time which required six weeks to perform manually took only six minutes for CHAMPION, yet comparable results were produced. Furthermore, the CHAMPION environment was constructed such that retargeting to a new adaptive computing system could be accomplished in just a few hours as opposed to weeks using manual methods. Thus, CHAMPION permits both ACSs and ASICs to be utilized by a wider audience and application development accomplished in less time
FFTPL: An Analytic Placement Algorithm Using Fast Fourier Transform for Density Equalization
We propose a flat nonlinear placement algorithm FFTPL using fast Fourier
transform for density equalization. The placement instance is modeled as an
electrostatic system with the analogy of density cost to the potential energy.
A well-defined Poisson's equation is proposed for gradient and cost
computation. Our placer outperforms state-of-the-art placers with better
solution quality and efficiency
Automated Netlist Generation for 3D Electrothermal and Electromagnetic Field Problems
We present a method for the automatic generation of netlists describing
general three-dimensional electrothermal and electromagnetic field problems.
Using a pair of structured orthogonal grids as spatial discretisation, a
one-to-one correspondence between grid objects and circuit elements is obtained
by employing the finite integration technique. The resulting circuit can then
be solved with any standard available circuit simulator, alleviating the need
for the implementation of a custom time integrator. Additionally, the approach
straightforwardly allows for field-circuit coupling simulations by
appropriately stamping the circuit description of lumped devices. As the
computational domain in wave propagation problems must be finite, stamps
representing absorbing boundary conditions are developed as well.
Representative numerical examples are used to validate the approach. The
results obtained by circuit simulation on the generated netlists are compared
with appropriate reference solutions.Comment: This is a pre-print of an article published in the Journal of
Computational Electronics. The final authenticated version is available
online at: https://dx.doi.org/10.1007/s10825-019-01368-6. All numerical
results can be reproduced by the Matlab code openly available at
https://github.com/tc88/ANTHE
The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement
Reinforcement learning (RL) for physical design of silicon chips in a Google
2021 Nature paper stirred controversy due to poorly documented claims that
raised eyebrows and attracted critical media coverage. The Nature paper
withheld most inputs needed to produce reported results and some critical steps
in the methodology. But two separate evaluations filled in the gaps and
demonstrated that Google RL lags behind human designers, behind a well-known
algorithm (Simulated Annealing), and also behind generally-available commercial
software. Crosschecked data indicate that the integrity of the Nature paper is
substantially undermined owing to errors in the conduct, analysis and
reporting.Comment: 14 pages, 1 figure, 3 table
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