Static timing analysis

Static timing analysis (STA) is a simulation method of computing the expected timing of a synchronous digital circuit without requiring a simulation of the full circuit. High-performance integrated circuits have traditionally been characterized by the clock frequency at which they operate. Measuring the ability of a circuit to operate at the specified speed requires an ability to measure, during the design process, its delay at numerous steps. Moreover, delay calculation must be incorporated into the inner loop of timing optimizers at various phases of design, such as logic synthesis, layout (placement and routing), and in in-place optimizations performed late in the design cycle. While such timing measurements can theoretically be performed using a rigorous circuit simulation, such an approach is liable to be too slow to be practical. Static timing analysis plays a vital role in facilitating the fast and reasonably accurate measurement of circuit timing. The speedup comes from the us

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Combining Structural and Timing Errors in Overclocked Inexact Speculative Adders

Mattia Cacciotti, Vincent Frédéric Camus, Christian Enz, Yu Jiang, Xun Jiao

Worst-case design is used in IoT devices and high performance data centers to ensure reliability, leading to a power efficiency loss. Recently, approximate computing has been proposed to trade off accuracy for efficiency. In this paper, we use Inexact Speculative Adders, which redesign the adder architecture to shorten its critical path and improve performance, but introduces controlled structural errors. On the other hand, overclocking is used to reduce conservative timing guardbands but could normally introduce catastrophic timing errors, we thus apply a supervised learning model to overclock speculative adders and predict their timing errors. We build a methodology to combine both structural and timing errors and analyze how they interplay with each other to limit the overal errors.

Ieee2017

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

Giovanni De Micheli, Pierre-Emmanuel Julien Marc Gaillardon, Hassan Ghasemzadeh Mohammadi, Majid Yazdani

With growing concern about process variation in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for design and verification. However, the high dimensionality of parameter space is a serious modeling challenge for emerging VLSI technologies, where the models are increasingly more complex. In this paper, we propose and validate a feature selection method to reduce the circuit modeling complexity associated with high parameter dimensionality. Despite the commonly used methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), this method is capable of dealing with mixed Gaussian and non-Gaussian parameters, and performs a parameter selection in the input space rather than creating a new space. By considering non-linear dependencies among input parameters and outputs, the method results in an effective parameter selection. The application of this method is demonstrated in digital circuit timing analysis to effectively reduce the number of simulations. The experimental results on Double-Gate Silicon NanoWire FET (DG-SiNWFET) technology indicate 2.5× speed up in timing variation analysis of the ISCAS89-s27 benchmark with a controlled average error bound of 9.4%.

2014

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