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Two challenges are combined when compressed tests are computed for path delay faults: 1) path delay faults that are important to detect are associated with long paths, and many of these faults are undetectable and 2) it may not be possible to compress a given test for a path delay fault. This paper addresses these challenges in the context of test data compression methods that are based on a linear-feedback shift-register (LFSR). The basic approach that this paper uses modifies initially random seeds for the LFSR into seeds that produce tests for detecting target faults. This approach can find seeds even if the available tests cannot be compressed into seeds. In addition, the procedure described in this paper also selects detectable path delay faults to address the presence of undetectable path delay faults in the set of target faults. For this purpose it uses a metric that measures the similarity between target and detected path delay faults, and attempts to compute seeds that increase the values of this metric. Experimental results are presented to demonstrate the ability of the procedure to detect path delay faults in benchmark circuits. [ABSTRACT FROM AUTHOR]

A functional test sequence for a design may not be effective as a manufacturing test for a logic block in the design because it achieves a low gate-level fault coverage. This paper describes a procedure for selecting a clock sequence that increases the gate-level fault coverage of a functional test sequence when it is used for testing a subset of logic blocks. The procedure deactivates the clocks to the logic blocks in the subset when a primary input vector has a negative effect on their fault coverage. The procedure is different from earlier test generation and test compaction procedures in that it increases the fault coverage without modifying the functional test sequence. It thus preserves some of the functional characteristics and the test application process for the sequence. Experimental results for benchmark circuits are presented to demonstrate the effectiveness of the procedure. [ABSTRACT FROM AUTHOR]

It was shown earlier that certain primary input values have a negative effect on the fault coverage of a functional test sequence when they appear repeatedly in the sequence. A gate-level sequential test generation procedure based on this observation computed a primary input cube c with preferred values, and generated random functional test sequences that conformed to c with a high probability 0.5 \leq p < 1 . This procedure selected values for p out of a set of possible values, and assigned the same value of p to all the primary inputs. Motivated by the low computational complexity of this procedure, this paper addresses the selection of p and the possibility of using different values of p for different primary inputs. The goal is to increase the fault coverage and reduce the number of functional test sequences. The procedure described in this paper adjusts a functional test sequence to a circuit by complementing values that conflict with c . The procedure requires fewer functional test sequences to reach or exceed the fault coverage of the earlier procedure for benchmark circuits. The procedure can be applied to any functional test sequence or set of functional test sequences. [ABSTRACT FROM AUTHOR]

This paper addresses theoretical aspects of arithmetic circuit verification based on algebraic rewriting. Its goal is to advance the understanding of algebraic techniques for arithmetic circuit verification in the context of symbolic computer algebra. The paper offers a new insight into the arithmetic circuit verification problem, by viewing the computation performed by the circuit as the flow of digital data. In the proposed bit-flow model, the circuit is modeled as a network of logic components satisfying a bit-flow conservation law. We prove that the value of the flow of data in the circuit is invariant throughout the circuit and use this to prove soundness and completeness of the rewriting technique, independently from the computer algebra arguments. The efficiency of the method is illustrated with impressive results for large integer multipliers. The verification system and benchmarks are offered in an open source software environment. [ABSTRACT FROM AUTHOR]

Exact synthesis is a versatile logic synthesis technique with applications to logic optimization, technology mapping, synthesis for emerging technologies, and cryptography. In recent years, advances in SAT solving have led to a heightened research effort into SAT-based exact synthesis. Advantages of exact synthesis include the use of various constraints (e.g., synthesis of emerging technology circuits). However, although progress has been made, its runtime remains unpredictable. This paper identifies two key points as hurdles to further progress. First, there are open questions regarding the design and implementation of exact synthesis systems, due to the many degrees of freedom. For example, there are different CNF encodings, different symmetry breaks to choose from, and different encodings may be suitable for different domains. Second, SAT-based exact synthesis is difficult to parallelize. Indeed, this is a common drawback of logic synthesis algorithms. This paper proposes four ways to close some open questions and to reduce runtime: 1) quantifying differences between CNF encoding schemes and their impacts on runtime; 2) demonstrating impact of symmetry breaking constraints; 3) showing how directed acyclic graph topology information can be used to decrease runtime; and 4) showing how topology information can be used to leverage parallelism. [ABSTRACT FROM AUTHOR]

3-D integrated circuits (3-D ICs) are expected to have shorter wirelength, better performance, and less power consumption than 2-D ICs. These benefits come from die stacking and use of through-silicon vias (TSVs) fabricated for interconnections across dies. However, the use of TSVs has several negative impacts such as area and capacitance overhead. To predict the quality of 3-D ICs more accurately, TSV-aware 3-D wirelength distribution models considering the negative impacts were developed. In this paper, we apply an optimal buffer insertion algorithm to the TSV-aware 3-D wirelength distribution models and present various prediction results on wirelength, delay, and power consumption of 3-D ICs. We also apply the framework to 2-D and 3-D ICs built with various combinations of process and TSV technologies and predict the quality of today and future 3-D ICs. [ABSTRACT FROM PUBLISHER]

Constructing algebraic polynomials using computer algebra techniques is believed to be state-of-the-art in analyzing gate-level arithmetic circuits. However, the existing approach applies algebraic rewriting directly to the gate-level netlist, which has potential memory explosion problem. This paper introduces an algebraic rewriting technique based on the and-inverter graph (AIG) representation of gate-level designs. Using AIG-based cut-enumeration and truth table computation, an efficient order of algebraic rewriting is identified, resulting in dramatic simplifications of the polynomial under construction. An automatic approach, which further reduces the complexity of algebraic rewriting by handling redundant polynomials, is also proposed. [ABSTRACT FROM AUTHOR]

Accurate delay analysis with distributed RC delay can be computationally expensive, and can contribute the majority of the total runtime for gate sizers. Recent works have shown that Lagrangian relaxation (LR)-based gate sizers have produced designs with the lowest power on average. But they are also very slow due to a large number of expensive timing updates spread across several tens of iterations. In this paper, we develop an LR-based discrete gate sizer for fast timing and power reduction. Our gate sizer is multithreaded and is equipped with parallelization enabling techniques, namely mutual exclusion edge (MEE) assignment and directed acyclic graph (DAG)-based netlist traversal (DNT). MEEs are dummy edges assigned to improve load sharing among different threads. DNT facilitates simultaneous resizing of gates belonging to different topological levels. Our Lagrange multiplier update strategy enables rapid convergence of our timing and power recovery algorithms. To reduce the runtime of timing updates, we propose a simple and fast-to-compute effective capacitance model. We further propose mechanisms to calibrate timing models to improve their accuracy. By calibrating the internal timing models only twice, our proposed gate sizing flow facilitates extremely fast design optimization. We benchmark our gate sizer using the ISPD 2012 and 2013 gate sizing contest benchmark suites. Compared to the state-of-the-art gate sizer, our proposed gate sizer is on average $15\times $ faster and the optimized designs have 2.5% higher leakage power. Since we tradeoff timing accuracy for larger runtime speedup, our optimized designs have small timing violations. [ABSTRACT FROM AUTHOR]

Recent developments in formal verification of arithmetic datapaths make efficient use of symbolic computer algebra algorithms. The circuit is modeled as an ideal in polynomial rings, and Gröbner basis (GB) reductions are performed over these polynomials to derive a canonical representation. As they model logic gates of the circuit, the ideals comprise largely of Boolean (or pseudo-Boolean) polynomials. This paper considers a logic synthesis analogue of GB reductions over Boolean polynomials, by interpreting symbolic algebra as the unate cube set algebra over characteristic sets. By representing Boolean polynomials as characteristic sets using zero-suppressed binary decision diagrams (ZDDs), implicit algorithms are efficiently designed for GB-reduction for datapath circuits. We show that the imposition of circuit-topology-based monomial orders exposes a special structure on the ZDD representation of the polynomials. The subexpressions employed in the GB-reduction are readily visible as subgraphs on the ZDDs, which are directly used to compose the result. Our division algorithms effectively cancel multiple monomials implicitly in one-step, simplify the search for divisors, and avoid intermediate size explosion. Experiments performed over various finite field arithmetic architectures demonstrate the efficiency of our algorithms and implementations; our approach is orders of magnitude faster as compared to conventional methods. [ABSTRACT FROM AUTHOR]

Gradient descent (GD) is a widely used optimization algorithm in machine learning. In this paper, a novel stochastic computing GD circuit (SC-GDC) is proposed by encoding the gradient information in stochastic sequences. Inspired by the structure of a neuron, a stochastic integrator is used to optimize the weights in a learning machine by its “inhibitory” and “excitatory” inputs. Specifically, two AND (or XNOR) gates for the unipolar representation (or the bipolar representation) and one stochastic integrator are, respectively, used to implement the multiplications and accumulations in a GD algorithm. Thus, the SC-GDC is very area- and power-efficient. As per the formulation of the proposed SC-GDC, it provides unbiased estimate of the optimized weights in a learning algorithm. The proposed SC-GDC is then used to implement a least-mean-square algorithm and a softmax regression. With a similar accuracy, the proposed design achieves more than $30 \times $ improvement in throughput per area (TPA) and consumes less than 13% of the energy per training sample, compared with a fixed-point implementation. Moreover, a signed SC-GDC is proposed for training complex neural networks (NNs). It is shown that for a 784-128-128-10 fully connected NN, the signed SC-GDC produces a similar training result with its fixed-point counterpart, while achieving more than 90% energy saving and 82% reduction in training time with more than $50 \times $ improvement in TPA. [ABSTRACT FROM AUTHOR]

Adiabatic logic is an energy-efficient technique, however, the time required in the design, validation, and debugging increases manifold for large-scale adiabatic system designs. In this endeavor, we present a hardware description language (HDL)-based modeling approach for 4-phase adiabatic logic design. The paper highlights the drawbacks of the existing approaches and proposes a new approach that captures the timing errors and detects the circuit’s invalid operation due to mutually exclusive inputs being violated. We develop a model library containing the function of the four periods used in the trapezoidal power-clock and the adiabatic logic gates. The validation and verification of the proposed approach were done on the ISO-14443 standard benchmark circuit, a 16-bit cyclic redundancy check (CRC) circuit. The system modeled using HDL shows the timing agreement with the transistor-level SPICE simulations. The novel use of the four periods of a power-clock improves the robustness and reliability for the design and verification of large adiabatic systems. [ABSTRACT FROM AUTHOR]

Near-threshold and sub-threshold voltage designs have been identified as possible solutions to overcome the limitations introduced by energy consumption in modern very large scale integration circuits. However, as we approach sub-10 nm transistor technology, aggressive voltage, and gate length scaling will reduce the reliability of logic circuits due to the increasing impact of noise and variability effects. Therefore, designers need new tools to simulate logic circuits in the presence of noise. Time-domain analysis helps understand how transient faults affect a circuit and can guide designers in producing noise-resistant circuitry. However, standard approaches to modeling intrinsic noise sources in the time domain are computationally expensive. Moreover, small noise-driven fluctuations in electron occupation of circuit nodes introduce time-varying biasing point fluctuations, increasing the modeling complexity. To address these challenges, this paper introduces a new approach to modeling thermal noise and random telegraph signal noise directly in the time domain by developing and solving a series of stochastic differential equations. In comparisons to traditional SPICE-based simulations, our approach can provide three orders of magnitude speedup in simulation time without sacrificing accuracy. Moreover, we introduce a novel, iterative threshold-crossing algorithm, aimed at the efficient sampling of rare noise transients. We show that Monte-Carlo simulations based on this approach can detect rare high-amplitude single event transients that would be impossible to uncover with standard transient simulators. [ABSTRACT FROM PUBLISHER]

SPICE offers an accurate method of simulating defect behavior. However, as demonstrated by recent research, it requires long computation time to simulate defect behavior, when considering process variation. To the best of our knowledge, there is no efficient variation-aware delay fault simulation methodology for resistive opens and resistive bridges. This paper presents a fast and accurate delay fault simulation methodology for these two defects. It is fast because it speeds up delay fault computation time by employing two efficient algorithms. The first algorithm is used to calculate transient gate output voltage, which is a key variable needed to compute delay faults. It employs a three step strategy to accelerate the computation of transient gate output voltage without compromising accuracy. The second algorithm uses bisection method to efficiently compute delay fault behavior of a fault-site. The proposed methodology (PM) has been incorporated in an open-source SPICE (NGSPICE) with BSIM4.7 transistor model. The methodology has been validated by comparing results with HSPICE using industrial designs from IWLS 2005 benchmarks and realistic fault-sites have been extracted from synthesized designs. Simulations are carried out using a 65-nm gate library (for illustration). When compared with HSPICE, results show that the PM is on average up to 52-times faster with \leq4.2\% error in accuracy for resistive open and 39-times faster with \leq5.2\% error in accuracy for resistive bridge defects. [ABSTRACT FROM PUBLISHER]

Delay fault simulation is an essential task during test pattern generation and reliability assessment of electronic circuits. With the high sensitivity of current nano-scale designs toward even smallest delay deviations, the simulation of small gate delay faults has become extremely important. Since these faults have a subtle impact on the timing behavior, traditional fault simulation approaches based on abstract timing models are not sufficient. Furthermore, the detection of these faults is compromised by the ubiquitous variations in the manufacturing processes, which causes the actual fault coverage to vary from circuit instance to circuit instance, and makes the use of timing accurate methods mandatory. However, the application of timing accurate techniques quickly becomes infeasible for larger designs due to excessive computational requirements. In this paper, we present a method for fast and waveform-accurate simulation of small delay faults on graphics processing units with exceptional computational performance. By exploiting multiple dimensions of parallelism from gates, faults, waveforms, and circuit instances, the proposed approach allows for timing-accurate and exhaustive small delay fault simulation under process variation for designs with millions of gates. [ABSTRACT FROM AUTHOR]

Unknown (X) values emerge during the design process as well as during system operation and test application. X-sources are for instance black boxes in design models, clock-domain boundaries, analog-to-digital converters, or uncontrolled or uninitialized sequential elements. To compute a test pattern for a given fault, well-defined logic values are required both for fault activation and propagation to observing outputs. In presence of X-values, conventional test generation algorithms, based on structural algorithms, Boolean satisfiability (SAT), or binary decision diagram-based reasoning may fail to generate test patterns or to prove faults untestable. This paper proposes the first efficient stuck-at and transition-delay fault test generation algorithm able to prove testability or untestability of faults in presence of X-values. It overcomes the principal pessimism of conventional algorithms when X-values are considered by mapping the test generation problem to the SAT of quantified Boolean formulas. Experiments on ISCAS benchmarks and larger industrial circuits investigate the increase in fault coverage for conventional deterministic and potential detection requirements for both randomized and clustered X-sources. [ABSTRACT FROM PUBLISHER]

Carbon nanotube field-effect transistors (CNFETs) are promising candidates for building energy-efficient digital systems at highly scaled technology nodes. However, carbon nanotubes (CNTs) are inherently subject to variations that reduce circuit yield, increase susceptibility to noise, and severely degrade their anticipated energy and speed benefits. Joint exploration and optimization of CNT processing options and CNFET circuit design are required to overcome this outstanding challenge. Unfortunately, existing approaches for such exploration and optimization are computationally expensive, and mostly rely on trial-and-error-based ad hoc techniques. In this paper, we present a framework that quickly evaluates the impact of CNT variations on circuit delay and noise margin, and systematically explores the large space of CNT processing options to derive optimized CNT processing and CNFET circuit design guidelines. We demonstrate that our framework: 1) runs over 100\boldsymbol \times faster than existing approaches and 2) accurately identifies the most important CNT processing parameters, together with CNFET circuit design parameters (e.g., for CNFET sizing and standard cell layouts), to minimize the impact of CNT variations on CNFET circuit speed with ≤5% energy cost, while simultaneously meeting circuit-level noise margin and yield constraints. [ABSTRACT FROM PUBLISHER]

To measure the variation of device $V_{t}$ requires long test for conventional wafer acceptance test (WAT) test structures. This paper presents a framework that can efficiently and effectively obtain the mean and variance of $V_{t}$ for a large number of designs under test (DUTs). The proposed framework applies the model-based random forest as its core model-fitting technique to learn a model that can predict the mean and variance of $V_{t}$ based only on the combined $I_{d}$ measured from parallel connected DUTs. The proposed framework can further minimize the total number of $I_{d}$ measurement required for prediction models while limiting their accuracy loss. The experimental results based on the SPICE simulation of a UMC 28-nm technology demonstrate that the proposed model-fitting framework can achieve a more than 99% ${R}$ -squared for predicting either $V_{t}$ mean or $V_{t}$ variance. Compared to conventional WAT test structures using binary search, our proposed framework can achieve a $120.3 \times$ speedup on overall test time for test structures with 800 DUTs. [ABSTRACT FROM AUTHOR]

Path delay faults are used for modeling small delay defects. Due to the large numbers of paths and the large numbers of undetectable path delay faults, test generation procedures for path delay faults use path selection procedures and procedures for the identification of undetectable faults to facilitate test generation. To complement these procedures, this paper describes a metric for assessing the likelihood that a path delay fault is detectable. Path selection procedures should prefer such faults in order to yield sets of target faults that are detectable even if not all the undetectable faults are identified prior to test generation. The metric is defined such that it allows all the path delay faults with the same value of the metric (the same likelihood of being detectable) to be enumerated together. The metric is computed based on the numbers of detections of transition faults under a test set for such faults, and requires N-detection fault simulation of transition faults for a sufficiently large value of N. The results of test generation for path delay faults confirm that faults with higher values of the metric are more likely to be detectable. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for isolating the failing paths for a fabricated chip. Failing paths are defined as the paths violating the timing constraint at the post-silicon stage, in the presence of process variations. To achieve this goal, a framework is suggested in which, first, a large number of statistically critical paths are extracted at the pre-silicon stage. These paths are those with the highest probability to violate the timing and therefore can form a “good” candidate set for failing paths. Next, a small set of representative paths are selected from those in the candidate set. These selected paths have their delays highly correlated with the delays of the remaining statistically critical paths. By directly measuring the delays of these selected representative paths at the post-silicon stage, the post-silicon delays of the remaining candidate failing paths can be predicted accurately that allows further isolating the failing paths for each fabricated chip. Simulation results show that up to a few thousand candidate failing paths can be accurately predicted using the post-silicon delays of less than 150 representative paths in the presence of more than 1000 independent process parameter variations for the ISCAS89 benchmark circuits. For each fabricated chip, the isolated failing paths are guaranteed to include all the “actual” failing paths in the candidate set. The proportion of the “actual” nonfailing paths from the isolated failing paths is shown to be very small. [ABSTRACT FROM PUBLISHER]

Invertible logic using a probabilistic magnetoresistive device model has been recently presented that can compute functions in bidirectional ways and solve several problems quickly, such as factorization and combinational optimization. In this article, we present a design framework for invertible logic circuits. Our approach makes use of linear programming to create a Hamiltonian library with the minimum number of nodes for small invertible-logic functions. In addition, as the device model is approximated based on stochastic computing in synthesizable SystemVerilog, a faster simulation using the compiled SystemC binary is realized than a conventional SPICE-level simulation and is verified using field-programmable gate array (FPGA) as prototyping. Using our design framework, several invertible-logic circuits are designed and emulated (verified) in SystemC, exhibiting five order-of-magnitude faster simulation than conventional work. [ABSTRACT FROM AUTHOR]

In recent years, there has been a lot of research into statistical static timing analysis (SSTA) to compute the critical path delay of a circuit under timing variation. In order to compute the true critical path delay, however, false paths that cannot be sensitized by any input vector must be identified first. Since SSTA is unable to capture the dynamic timing behavior of a circuit, it is completely blind to false paths, and thus it overestimates the circuit timing. In this paper, we propose a new concept of timing analysis approach called statistical dynamic timing analysis (SDTA), which is able to precisely express the statistical behavior of dynamic transitions at the output of gate into a compact form and to directly evaluate and propagate the expressions throughout the circuit, by which the false paths can be cleaned effectively. In addition, to be practical, we propose a couple of techniques that enable a fast computation of the SDTA. We tested the proposed approach on ISCAS benchmarks and carry skip adders under timing variation to show its accuracy in computing the distribution of the true critical path delay of a circuit. In summary, compared to the previous approach of false path-aware statistical timing analysis, our timing analysis technique is able to reduce the accuracy error in the mean and standard deviation of true critical path delay distribution from 9.8% to 1.9% and from 29.4% to -3.4\%, respectively. [ABSTRACT FROM AUTHOR]

General purpose computing on graphical processing units (GPGPU) is a paradigm shift in computing that promises a dramatic increase in performance. GPGPU also brings an unprecedented level of complexity in algorithmic design and software development. In this paper, we present an efficient parallel fault simulator, FSimGP^2, that exploits the high degree of parallelism supported by a state-of-the-art graphic processing unit (GPU) with the NVIDIA compute unified device architecture. A novel 3-D parallel fault simulation technique is proposed to achieve extremely high computation efficiency on the GPU. Global communication is minimized by concentrating as much work as possible on the local device's memory. We present results on a GPU platform from NVIDIA (a GeForce GTX 285 graphics card) that demonstrate a speedup of up to 63\times and 4\times compared to two other GPU-based fault simulators and up to 95\times over a state-of-the-art algorithm on conventional processor architectures. [ABSTRACT FROM AUTHOR]

Considerable effort has been expended in the electronic design automation community in trying to cope with the statistical timing problem. Most of this effort has been aimed at generalizing the static timing analyzers to the statistical case. On the other hand, detailed transistor-level simulations of the critical paths in a circuit are usually performed at the final stage of performance verification. We describe a transistor-level Monte Carlo (MC) technique which makes final transistor-level timing verification practically feasible. The MC method is used as a golden reference in assessing the accuracy of other timing yield estimation techniques. However, it is generally believed that it can not be used in practice as it requires too many costly transistor-level simulations. We present a novel approach to constructing an improved MC estimator for timing yield which provides the same accuracy as standard MC but at a cost of much fewer transistor-level simulations. This improved estimator is based on a unique combination of a variance reduction technique, importance sampling, and a cheap but approximate gate delay model. The results we present demonstrate that our improved yield estimator achieves the same accuracy as standard MC at a cost reduction reaching several orders of magnitude. [ABSTRACT FROM AUTHOR]

This work reports a compact behavioral model for gated-synaptic memory. The model is developed in Verilog-A for easy integration into computer-aided design of neuromorphic circuits using emerging memory. The model encompasses various forms of gated synapses within a single framework and is not restricted to only a single type. The behavioral theory of the model is described in detail along with a full list of the default parameter settings. The model includes parameters, such as a device’s ideal set time, threshold voltage, general evolution of the conductance with respect to time, decay of the device’s state, etc. Finally, the model’s validity is shown via extensive simulation and fitting to experimentally reported data on published gated-synapses. [ABSTRACT FROM AUTHOR]

A macromodeling technique for digital cells is presented. The proposed macromodel allows fast and accurate signal approximation, as well as delay estimation for CMOS digital circuits. To verify its accuracy, the technique is implemented on different logic gates, a half-adder, and a flip-flop. The simulation results are compared with those obtained with SPICE for a 65-nm CMOS technology with a 1.0-V power supply. The results show the delay offset and amplitude deviation are within 5% of SPICE values for an exponential input with maximum $10^{9}$ V/s slope. The execution time of the experimental simulator implemented in MATLAB was more than 1000 times faster than SPICE in all cases. [ABSTRACT FROM AUTHOR]

Long Short Term Memory (LSTM) networks are a class of recurrent neural networks that are widely used for machine learning tasks involving sequences, including machine translation, text generation, and speech recognition. Large-scale LSTMs, which are deployed in many real-world applications, are highly compute intensive. To address this challenge, we propose AxLSTM, an application of approximate computing to improve the execution efficiency of LSTMs. An LSTM is composed of cells, each of which contains a cell state along with multiple gating units that control the addition and removal of information from the state. The LSTM execution proceeds in timesteps, with a new symbol of the input sequence processed at each timestep. AxLSTM consists of two techniques—Dynamic Timestep Skipping (DTS) and Dynamic State Reduction (DSR). DTS identifies, at runtime, input symbols that are likely to have little or no impact on the cell state and skips evaluating the corresponding timesteps. In contrast, DSR reduces the size of the cell state in accordance with the complexity of the input sequence, leading to a reduced number of computations per timestep. We describe how AxLSTM can be applied to the most common application of LSTMs, viz., sequence-to-sequence learning. We implement AxLSTM within the TensorFlow deep learning framework and evaluate it on 3 state-of-the-art sequence-to-sequence models. On a 2.7 GHz Intel Xeon server with 128 GB memory and 32 processor cores, AxLSTM achieves $ {1.08\times -1.31 \times }$ speedups with minimal loss in quality, and $ {1.12 \times -1.37 \times }$ speedups when moderate reductions in quality are acceptable. [ABSTRACT FROM AUTHOR]

Leakage currents are one major concern when designing recent CMOS devices, making design for leakage at all stages of the design process mandatory. Early leakage optimization requires early leakage prediction, and for electronic system level design, this means estimation capabilities at register transfer (RT) level or above. Existing models are very accurate, but slow [transistor level such as Berkeley Simulator (BSIM)], or the slightly faster gate level models (such as the Liberty library), disregard relevant parameters. We present RT level leakage macro models, which are faster than recent gate level models, while preserving the accuracy of the transistor level models to a great extent. An estimation framework is proposed, describing the subthreshold, gate, and junction leakage of recent technology devices. The models are characterized using BSIM compact models and a Monte Carlo process variation description. Each varying BSIM parameter can be described. As an example of use, channel length, oxide thickness, and channel doping are regarded together with the temperature, supply voltage and body voltage. The final macro model needs less than a hundred parameters to capture the leakage behavior of an entire RT component and is still analytically describing the dependence to the process parameters. Compared to SPICE + BSIM, a model prediction is computed up to a hundred times faster for large RT components, and is, depending on the analyzed technology, within 2.1% (for 16-nm LP)–6.8% (for 65-nm bulk) deviation over a wide range of operating conditions and process variation settings. [ABSTRACT FROM AUTHOR]