Runtime variability sources, such as bias temperature instability (BTI) and supply voltage fluctuation affect both timing and functionality of the flip-flops inside a VLSI circuit. In this paper, we propose a method to improve the timing and reliability of the VLSI circuits by optimizing the flip-flops for resiliency against aging and supply voltage fluctuation. In the proposed selective reliability optimization method, we first extend the standard cell libraries by adding optimized versions of the flip-flops designed for better resiliency against severe BTI impact and/or supply voltage fluctuation. Then, we optimize the VLSI circuit by replacing the aging-critical and voltage-drop-critical flip-flops (VC) of the circuit with the reliability-optimized versions to improve the timing and the reliability of the entire circuit in a cost-effective way. The simulation results show that incorporating the optimized flip-flops in a processor can prolong the lifetime of the processor by 36.9% compared to the original design, which translates into better reliability. This is achieved with negligible leakage overhead (less than 0.1% on the processor) and no area overhead which facilitates the integration of the proposed method in the standard VLSI design flow.

Scaling supply voltage to the near-threshold voltage (NTV) region is an effective approach for energy-constrained circuit design at the cost of acceptable performance reduction. However, by operating in the NTV region, the sensitivity of circuits to process and runtime variations significantly aggravates. Therefore, the performance and power consumption of a circuit is largely impacted by the variabilities, which affects the operating voltage for the most efficient computation, i.e., the minimum energy point (MEP). Accordingly, finding an optimum operating voltage for near-threshold computing (NTC) to account for variabilities is very challenging. In this article, we propose an MEP calibration and adaptation approach based on machine learning to tune for minimal energy operation on a per-chip basis by considering process and runtime variations. In the proposed approach, the optimal supply voltage of each chip is determined during manufacturing tests by characterizing dynamic and leakage power and at runtime by considering the impact of temperature variation. The presented method does not require costly power measurement circuitry on chip. The simulation results show that the proposed method has high MEP prediction accuracy and achieves near-optimal operation by only 1.2% higher energy consumption compared with the optimal operation.

For applications with stringent power budget, such as ultra low power systems and Internet of the things (IoT), power and energy are the most important constraints. It is shown that when the supply voltage is close to the threshold voltage of transistor, known as near threshold computing (NTC), the energy consumption is at its minimum range. However, by reducing the supply voltage not only the circuit performance decreases significantly, but aggravates various reliability mechanisms. Moreover, the performance variation due to process and runtime variation increases exponentially which makes the traditional margining, to address variability, very inefficient. In this paper, we address energy-efficient countermeasures to combat reliability challenges at NTC in order to guarantee resilient and energy-efficient system operation. We propose to partition a functional unit such as Arithmetic Logic Unit (ALU) into multiple smaller and faster functional units and power-gate them whenever they are not used for long time. Simulation results show that by applying the proposed method the energy efficiency of an ALU can be improved by up to 43.4% at NTC with multiple fold reliability improvements due to timing failures.

Radiation-induced soft errors are a major reliability concern in circuits fabricated at advanced technology nodes. Online soft-error vulnerability estimation offers the flexibility of exploiting dynamic fault-tolerant mechanisms for cost-effective reliability enhancement. We propose a generic run-time method with low area and power overhead to predict the soft-error vulnerability of on-chip memory arrays as well as logic cores. The vulnerability prediction is based on signal probabilities (SPs) of a small set of flip-flops, chosen at design time, by studying the correlation between the soft-error vulnerability and the flip-flop SPs for representative workloads. We exploit machine learning to develop a predictive model that can be deployed in the system in software form. Simulation results on two processor designs show that the proposed technique can accurately estimate the soft-error vulnerability of on-chip logic core, such as sequential pipeline logic and functional units as well as memory arrays that constitute the instruction cache, the data cache, and the register file.

Run-time solutions based on online monitoring and adaptation are required for resilience in nanoscale integrated circuits, as design-time solutions and guard bands are no longer sufficient. Bias temperature instability-induced transistor aging, one of the major reliability threats in nanoscale very large scale integration, degrades path delay over time and may lead to timing failures. Chip health monitoring is, therefore, necessary to track delay changes on a per-chip basis over the chip lifetime operation. However, direct monitoring based on actual measurement of path delays can only track a coarse-grained aging trend in a reactive manner, not suitable for proactive fine-grain adaptations. In this paper, we propose a low cost and fine-grained workload-induced stress monitoring approach, based on machine learning techniques, to accurately predict aging-induced delay. We integrate space and time sampling of selective flip-flops into the runtime monitoring infrastructure in order to reduce the cost of monitoring the workload. The prediction model is trained offline using support-vector regression and implemented in software. This approach can leverage proactive adaptation techniques to mitigate further aging of the circuit by monitoring aging trends. Simulation results for realistic open-source benchmark circuits highlight the accuracy of the proposed approach.

In advanced technology nodes, bias temperature instability (BTI) has emerged as a prominent reliability concern. The worst-case effects of BTI occur during specific workload phases in which flip-flops (FFs) on a critical path do not switch their logic values for a long duration. These inactive FFs in the circuit experience accelerated workload-dependent static-BTI (S-BTI) stress. The aging effect of S-BTI for a few hours has been shown to be equivalent to one year of aging due to dynamic BTI, which can eventually cause circuit failure. The techniques available to mitigate S-BTI stress during standby mode of circuits are pessimistic, thereby limiting the performance of the circuit. To address this problem, we propose a runtime monitoring method to raise a flag when a timing-critical FF experiences severe S-BTI stress. To reduce the monitoring costs, we select a small representative set of FFs offline based on workload-aware correlation analysis and these selected FFs are monitored online for static aging phases. Our experiments conducted on two processors show that less than 0.5% of the total number of FFs is required to be selected as representative FFs for S-BTI stress monitoring. We also propose a low-overhead mitigation scheme to relax critical FFs by executing a software subroutine that is designed to exercise critical FFs.

In advanced technology nodes, Bias Temperature Instability (BTI) has emerged as a prominent reliability concern. The worst-case effects of BTI occur during specific workload phases in which flip-flops on a critical path do not switch their logic values for a long duration. These inactive flip-flops in the circuit experience accelerated workload-dependent static-BTI stress. The aging effect of static BTI for a few hours has been shown to be equivalent to one year of aging due to dynamic BTI, which can eventually cause circuit failure. The techniques available to mitigate static-BTI stress during standby mode of circuits are pessimistic, thereby limiting the performance of the circuit. To address this problem, we propose a runtime monitoring method to raise a flag when a timing-critical flip-flop experiences severe static-BTI stress. To reduce the monitoring costs, we select a small representative set of flip-flops offline based on workload-aware correlation analysis and these selected flip-flops are monitored online for static aging phases. Our experiments conducted on two processors show that, less than 0.5% of the total number of flip-flops is required to be selected as representative flip-flops for S-BTI stress monitoring.

Near threshold computing (NTC) through aggressive supply voltage scaling has the potential to significantly improve energy-efficiency. However, the increase in variation-induced timing errors is a major challenge in NTC. This can be addressed in the scope of approximate computing by selectively embracing non-important variation-induced timing errors. In this paper, we propose a framework to leverage the error tolerance potential of approximate computing for energy-efficient NTC designs. In our framework, statistical timing error analysis as well as structural and functional error propagation analysis is performed to identify the approximable portion of a design. Then, a mixed-timing logic synthesis is employed to improve energy-efficiency by embracing errors in the approximable portion of the design. Experimental results show that the proposed approach can improve the energy-efficiency of NTC designs by more than 30%.

The start-up value of SRAM cells can be used as the random number vector or a seed for the generation of a pseudo random number. However, the randomness of the generated number is pretty low since many of the cells are largely skewed due to process variation and their start-up value leans toward zero or one. In this paper, we propose an approach to increase the randomness of SRAM-based True Random Number Generators (TRNGs) by leveraging transistor aging impact. The idea is to iteratively power-up the SRAM cells and put them under accelerated aging to make the cells less skewed and hence obtaining a more random vector. The simulation results show that the min-entropy of SRAM-based TRNG increases by 10X using this approach.

The reliability of digital integrated circuits is becoming the primary design concern in advanced technology nodes. The accelerated transistor aging mechanisms, such as Bias Temperature Instability, reduce the noise margin of memory cells leading to increased failure rate. Traditionally, error correction codes, such as Hamming code, are widely used to detect and correct transient errors in memory arrays, due to radiation-induced soft errors. In this work, we propose a new coding scheme which is able to reduce the overall aging rate of the memory arrays, by balancing the signal probabilities of the stored values. The optimal coding scheme is found with the help of an optimization technique which evolves a systematic Hamming code into an aging-aware non-systematic code. This technique is applicable to different memory arrays such as caches. The simulation results on different workloads show 45.2% reduction in aging induced Static Noise Margin (SNM) degradation compared to the conventional methods.

Power and energy reduction is of uttermost importance for applications with stringent power/energy budget such as ultralow power and energy-harvested systems. Aggressive voltage scaling and in particular near-threshold computing is a promising approach to reduce the power and energy consumption. However, reducing the supply voltage leads to drastic performance variation induced by process and runtime variation. Temperature variation is one of the major sources of performance variation. In this paper, we study the impact of temperature variation on the circuit behavior in the near-threshold voltage region and show that the ambient temperature has a huge impact on the metrics such as circuit delay, power, and energy consumption. We also propose a low-cost, ambient temperature-aware voltage scaling technique to reduce the unnecessary energy overhead caused by temperature variation. Simulation results show that our proposed approach reduces the energy consumption by more than 1.95×.

Due to recent technology scaling trends and increased circuit complexity, process and runtime variabilities are becoming major threats for correct circuit operation. Among these, transient voltage fluctuations appear to be the most critical issue, accounting for the biggest component of timing margin, at increased cost. As various design and workload parameters have an impact on voltage fluctuations, they need to be fully understood in order to design efficient countermeasures and margining. FPGAs are predestined for this analysis by allowing more control over such experiments at lower cost than ASICs. Even more, they highly suffer from the same issues, which are typically only handled by excessive and over-pessimistic timing margining built into the mapping tools. In this work, we implemented and calibrated sensors in configurable logic appropriate to observe delay changes caused by transient voltage fluctuations. We place them at multiple locations on the chip to evaluate temporal and spatial changes in timing margin due to different workload-characteristics. This analysis provides useful insights to designers for application mapping and workload scheduling.

Power/energy reduction is of uttermost importance for applications with stringent power/energy budget such as ultra-low power and energy-harvested systems. Aggressive voltage scaling and in particular Near-Threshold Computing (NTC) is a promising approach to reduce the power and energy consumption. However, reducing the supply voltage leads to drastic performance variation induced by process and runtime variation. Temperature variation is one of the major sources of performance variation. In this paper, we study the impact of temperature variation on the circuit behavior at near threshold region and show that the ambient temperature has a huge impact on the metrics such as circuit delay, power and energy consumption. We also propose a low-cost, ambient temperature-aware voltage scaling technique to reduce the unnecessary energy overhead caused by temperature variation. Simulation results show that our proposed approach reduces the energy consumption by more than 3X.

Energy constrained systems become the cornerstone of emerging energy harvested or battery-limited applications in Internet of Thing (IoT) platforms. A promising approach is to operate at near threshold voltage ranges, which can significantly reduce energy per operation. However, due to increased sensitivity to variations and reduced noise margin at low voltages, resiliency becomes a major challenge. In this paper we provide a cross layer approach, from compiler all the way to circuit design, to maximize the energy efficiency as well as the resiliency of functional units. The key idea is to identify the instructions which become timing critical at low voltages and address them by a combination of circuit redesign, multi-cycle execution and code replacement. This allows us to significantly reduce timing failures and at the same time limit leakage energy, which becomes considerable at low voltages. This approach enables resilient and energy efficient operation in a wide voltage range to trade off energy and performance.

Near threshold computing (NTC) has the potential to reduce the energy consumption by orders of magnitude. However, NTC designs suffer from a higher sensitivity to process variation and substantial performance degradation. In NTC, process variation affects the delays of different pipeline stages significantly, resulting in energy-inefficient designs. In this paper, we propose an energy-efficient variation-aware processor pipeline optimization, in which the pipeline stages are balanced by considering the impact of process variation during earlier design phases. This can lead to a well-balanced design and significant improvement in energy-efficiency. For this purpose, we employ an iterative variation-aware synthesis flow in which the synthesis tool is provided with variation information. Since the impact of process variation is considered during synthesis, our technique can improve the energy-efficiency by avoiding pessimistic guard band. Simulation results show that our technique can improve the energy-efficiency of OpenSPARC and FabScalar cores by 55% and 85%, respectively.

With CMOS technology downscaling into the nanometer regime, the reliability of SRAM memories is threatened by accelerated transistor aging mechanisms such as Bias Temperature Instability (BTI). BTI leads to a considerable degradation of SRAM cell Static Noise Margin (SNM), which increases the memory failure rate. Since BTI is workload dependent, the aging rates of different cells in a memory array are quite non-uniform. To address this issue, a variety of bit-flipping techniques has been proposed to decrease the SNM degradation by balancing the signal probabilities of the cells. However, existing bit-flipping techniques impose too much area and power overhead as at least an additional column is required to store the inversion flags. In this paper, we propose a low cost self-controlled bit-flipping technique which inverts all bit positions with respect to an existing bit. This technique is applied to a register-file and cache units of an embedded microprocessor. Our simulation results show that the reliability of the proposed technique is similar to that of existing bit-flipping techniques, while imposing 64% less area overhead.

Bias Temperature Instability (BTI) affects both timing and functionality of Flip-Flops (FFs). In a typical processor, a considerable portion of FFs always operate under severe BTI stress independent of the running workload. This leads to a serious timing degradation in these FFs, and to avoid timing violations in field, they mandate a large aging guardband (timing margin). In this paper, we propose a method to mitigate the BTI-induced aging of such FFs via transistor sizing optimization. The optimized FFs are more resilient against BTI stress compared to the original ones. The imposed overall leakage is negligible, and the area of the optimized FFs is similar to the original ones in order to facilitate the replacement of the original FFs with optimized alternatives in the circuit layout. Simulation results show that incorporating the optimized FFs in a processor can reduce the timing guardband of the processor by 22.8% compared to the original design, which translates into prolonged lifetime and more reliability.

Accelerated transistor aging is one of the major unreliability sources at nano-scale technology nodes. Aging causes the circuit delay to increase and eventually leads to timing failures. Since aging is dependent on various factors such as temperature and workload, the aging rates of different components of the circuit are non-uniform. However, timing failures start to occur once the most-aged part fails to meet the timing constraint. In this paper, we present a cross-layer aging mitigation methodology from device level up to architecture level by balancing the delays of different parts of the design at the desired lifetime rather than at design time. Our results show that the proposed approach can efficiently prolong the system lifetime with a negligible impact on area and power.

In the deep nanoscale regime, process and runtime variations have emerged as the major sources of uncertainty and unpredictability in circuit operation. Static mitigation approaches do not consider the dependence of variations on workload and chip usage, while adaptive techniques do not incorporate detailed circuit-level information. We propose a fine-grained adaptive technique in which machine learning is exploited to perform circuit clustering and obtain a representative for each cluster. By monitoring the representative in each cluster at runtime, performance variations in the entire cluster can be tracked such that appropriate fine-grained adaptation can be applied to each cluster. Experimental results for ISCAS'89, IWLS'05, and ITC'99 benchmarks as well as the LEON processor show that the proposed approach introduces negligible overhead significantly extends circuit lifetime, facilitates higher operating frequencies, and reduces the leakage power.

This paper presents a comprehensive analysis of radiation-induced soft errors of SRAMs designed in SOI FinFET technology. For this purpose, we propose a cross layer approach starting from a 3D simulation of particle interactions in FinFET structures up to circuit level analysis by considering the layout of the memory array. This approach enables us to consider the effect of different factors such as supply voltage and process variation on Soft Error Rate (SER) of FinFET SRAM memory arrays. Our analysis shows that proton-induced soft errors are becoming important and comparable to the SER induced by alpha-particles especially for low supply voltages (low power applications). Moreover, we observe that the ratio of Multiple Bit Upset (MBU) to Single Event Upset (SEU) for alpha-particle radiation is much higher than that of proton.

Transistor aging, mostly due to Bias Temperature Instability (BTI), is one of the major unreliability sources at nano-scale technology nodes. BTI causes the circuit delay to increase and eventually leads to a decrease in the circuit lifetime. Typically, standard cells in the library are optimized according to the design time delay, however, due to the asymmetric effect of BTI, the rise and fall delays might become significantly imbalanced over the lifetime. In this paper, the BTI effect is mitigated by balancing the rise and fall delays of the standard cells at the excepted lifetime. We find an optimal tradeoff between the increase in the size of the library and the lifetime improvement (timing margin reduction) by non-uniform extension of the library cells for various ranges of the input signal probabilities. The simulation results reveal that our technique can prolong the circuit lifetime by around 150% with a negligible area overhead.

Modern Field Programmable Gate Arrays (FPGAs) are built using the most advanced technology nodes to meet performance and power demands. This makes them susceptible to various reliability challenges at nano-scale, and in particular to transistor aging. In this paper, an experimental analysis is made to identify the main parameters and phenomena influencing the performance degradation of FPGAs. For that purpose, a set of controlled ring-oscillator-based sensors with different frequencies and tunable activity control are implemented on a Spartan-6 FPGA. Thus, the internal switching activities (SAs) and signal probabilities (SPs) of the sensors can be varied. We performed accelerated-lifetime conditions using elevated temperatures and voltages in a controlled setting to stress the FPGA. A novel monitoring method based on measuring the electromagnetic emissions of the FPGA is used to accurately monitor the performance of the sensors before and after the stress. The experiments reveal the extent of performance degradations, the impact of SPs and SAs, and the relative impacts of BTI and HCI aging factors.

In nano-scale regime, there are various sources of uncertainty and unpredictability of VLSI designs such as transistor aging mainly due to Bias Temperature Instability (BTI) as well as Process-Voltage-Temperature (PVT) variations. BTI exponentially varies by temperature and the actual supply voltage seen by the transistors within the chip which are functions of leakage power. Leakage power is strongly impacted by PVT and BTI which in turn results in thermal-voltage variations. Hence, neglecting one or some of these aspects can lead to a considerable inaccuracy in the estimated BTI-induced delay degradation. However, a holistic approach to tackle all these issues and their interdependence is missing. In this paper, we develop an analytical model to predict the probability density function and covariance of temperatures and voltage droops of a die in the presence of the BTI and process variation. Based on this model, we propose a statistical method that characterizes the life-time of the circuit affected by BTI in the presence of process-induced temperature-voltage variations. We observe that for benchmark circuits, treating each aspect independently and ignoring their intrinsic interactions results in 16% over-design, translating to unnecessary yield and performance loss.

In the nanometer era, runtime variations due to workload dependent voltage and temperature variations as well as transistor aging introduce remarkable uncertainty and unpredictability to nanoscale VLSI designs. Consideration of short-term and long-term workload-dependent runtime variations at design time and the interdependence of various parameters remain as major challenges. Here, we propose a static timing analysis framework to accurately capture the combined effects of various workload-dependent runtime variations happening at different time scales, making the link between system-level runtime effects and circuit-level design. The proposed framework is fully integrated with existing commercial EDA toolset, making it scalable for very large designs. We observe that for benchmark circuits, treating each aspect independently and ignoring their intrinsic interactions is optimistic and results in considerable underestimation of timing margin.

We propose a novel custom instruction (CI) selection technique for process variation and transistor aging aware instruction-set architecture synthesis. For aggressive clocking, we select CIs based on statistical static timing analysis (SSTA), which achieves efficient speedup during target lifetime while mitigating degradation of timing yield (i.e., probability of satisfying the timing). Furthermore, we consider process variation and aging on not only CIs but also basic instructions (BIs). Even if basic functional units (BFUs), e.g., ALU, get slower due to aging, only a few BIs with critical propagation delay may violate the timing, whereas the other BIs running on the same BFU can still satisfy the timing. We then introduce “customized BFUs”, which execute only such aging-critical BIs. The customized BFUs, used as spare BFUs of the aging-critical BIs, can extend lifetime of the system. Combining the two approaches enables speedup as well as lifetime extension with no or negligibly small area/power overhead. Experiments demonstrate that our work outperforms conventional worst-case work (by an average speedup of about 49%) and existing SSTA-based work (16x or more lifetime extension with comparable speedup).

Power Supply Noise (PSN) has emerged as an important resilience issue in nano-scale CMOS technology. Due to simultaneous switching of various gates, the actual supply voltage seen by individual gates inside the circuit might be lower than the nominal supply voltage, leading to extra delays. Since in at-speed scan testing simultaneous switchings are higher than the functional mode, test invalidation due to excessive PSN can happen, which may impact yield loss. In this paper, we propose a Linear Programming-based X-filling approach to minimize PSN in at-speed scan test by assigning appropriate values to X-bits in partially specified test patterns. In this paper, spatial and transition time correlations due to circuit layout, power mesh, and netlist are taken into account to increase the accuracy of dynamic PSN estimation and for the first time the delay of the circuit is directly targeted to minimize the effect of PSN during the at-speed scan test.

Bias Temperature Instability (BTI) plays a significant role in transistor aging. As the device dimensions shrink due to technology scaling, this problem poses serious reliability issues. Field Programmable Gate Arrays (FPGAs) use very advanced nano-scaled CMOS technologies, which makes them vulnerable to BTI-induced aging. Previous studies have analyzed the relationship between the configuration of Look-Up Tables (LUTs) and the input signal probabilities against BTI-induced aging of LUTs. In this paper, we propose two methods to mitigate BTI-induced aging in LUTs. The mitigation is performed by manipulating the configuration of the used LUTs and their input signal probabilities, while maintaining the functionality of the mapped design. We implemented the proposed methods using the academic tool Verilog to Routing (VTR). The experimental results show that our methods can mitigate BTI-induced aging of LUT substantially and improve the lifetime of the FPGA-mapped designs, on average, by more than 200%.

As CMOS technology scales down into the nanometer regime, designers have to add pessimistic timing margins to the circuit as guardbands to avoid timing violations due to various reliability effects, in particular accelerated transistor aging. Since aging is workload-dependent, the aging rates of different paths are non-uniform, and hence, design time delay-balanced circuits become significantly unbalanced after some operational time. In this paper, an aging-aware logic synthesis approach is proposed to increase circuit lifetime with respect to a specific guardband. Our main objective is to optimize the design timing with respect to post-aging delay in a way that all paths reach the assigned guardband at the same time. In this regard, in an iterative process, after computing the post-aging delays, the lifetime is improved by putting tighter timing constraints on paths with higher aging rate and looser constraints on paths which have less post-aging delay than the desired guarband. The experimental results shows that the proposed approach improves circuit lifetime in average by more than 3X with negligible impact on area. Our approach is implemented on top of a commercial synthesis toolchain, and hence scales very well.

With continuous downscaling of VLSI technologies, logic cells are becoming more susceptible to radiation-induced soft error. To accurately model this at chip-level, the impact of electrical masking should be accurately considered. Moreover, increasing complexity of VLSI chips at nanoscale results in voltage fluctuation across the chip which impacts the electrical masking. In this paper, we present a chip-level electrical masking analysis which accurately considers the impact of voltage fluctuation across the chip. Our analysis shows that neglecting voltage fluctuation in electrical masking can lead up to 152% inaccuracy in the overall soft error rate. We also present a technique based on backward pulse propagation to reduce the runtime of this analysis.

Transistor aging is a major reliability concern for nanoscale CMOS technology that can significantly reduce the operation lifetime of very large-scale integration chips. Negative bias temperature instability (NBTI) is a major contributor to transistor aging that affects pMOS transistors. On the other hand, leakage power is becoming a dominant factor of the total power with successive technology scaling. Since the input combinations applied to a logic core have a significant impact on both NBTI and leakage power, input vector control can be used to optimize both phenomena during idle cycles. In this paper, we present an efficient input vector selection technique based on linear programming for cooptimizing the NBTI-induced delay degradation and leakage power consumption during standby mode. Since the NBTI-induced delay degradation and leakage power are not affected by the input vector in the same direction, we provide a pareto curve based on both phenomena. A suitable point from such a pareto curve is chosen based on circuit conditions and requirements during runtime.

Negative Bias Temperature Instability (NBTI) is a major source of transistor aging in scaled CMOS, resulting in slower devices and shorter lifetime. NBTI is strongly dependent on the input vector. Moreover, a considerable fraction of execution time of an application is spent to execute NOP (No Operation) instructions. Based on these observations, we present a novel NOP assignment to minimize NBTI effect, i.e. maximum NBTI relaxation, on the processors. Our analysis shows that NBTI degradation is more impacted by the source operands rather than instruction opcodes. Given this, we obtain the instruction, along with the operands, with minimal NBTI degradation, to be used as NOP. We also proposed two methods, software-based and hardware-based, to replace the original NOP with this maximum aging reduction NOP. Experimental results based on SPEC2000 applications running on a MIPS processor show that this method can extend the lifetime by 37% in average while the overhead is negligible.

Transistor aging mostly due to Negative and Positive Bias Temperature Instability (NBTI and PBTI) is a major reliability threat for VLSI circuits fabricated in nanometer technology nodes. As much as FPGAs benefit from the most scaled and advanced technologies, they become more susceptible to transistor aging. In this paper, we investigate the effect of transistor aging on programmable routing resources of FPGAs, by considering different implementations through detailed SPICE simulations. The effects of different parameters, such as wire length, cascaded routing, routing fan-out, signal probability and supply voltage on the aging of routing resources are studied.

As CMOS feature size scales to the nanometer regime, transistor aging mostly due to Negative Bias Temperature Instability (NBTI) and Hot Carrier Injection (HCI), has emerged as a major reliability concern. Threshold voltage shift causes the circuit to fail, once the post-aging delay exceeds the timing constraint. In this paper, we investigate the stacking effect of transistors on aging and propose a novel input/transistor reordering approach to alleviate the effect of NBTI and HCI during the active mode operation of the circuit. According to the results, the circuit failing due to aging effect is postponed by increasing the operational lifetime for ISCAS benchmarks by 23.6%, in average, while it has a negligible effect on delay, area, and power compared to the original cell input ordering.

Transistor aging mostly due to Negative and Positive Bias Temperature Instability (NBTI and PBTI) is a major reliability threat for VLSI circuits fabricated in nanometer technology nodes. These phenomena can shift the threshold voltage of transistor over time, increase their delays and cause timing failure and ultimately reduction of lifetime of VLSI chips. As much as FPGAs benefit from the most scaled and advanced technologies, they become susceptible to transistor aging. In this paper, we investigate the effect of transistor aging, due to NBTI and PBTI, in look-up tables (LUTs), by considering different implementations through detailed SPICE simulations. We found out that the delay degradation due to transistor aging depends on the mapped configuration, usage (input signal probability) as well as the specific LUT implementation. Moreover, the specific configuration mapped previously into an LUT has a considerable effect on the delay degradation of the currently used configuration of that LUT. We also found that the all-zero configuration which is normally used as the standby configuration is not the best choice and it may even result in high delay degradation.

Transistor aging is a serious reliability challenge for nanoscale CMOS technology which can significantly reduce the operation lifetime of VLSI chips. Negative Bias Temperature Instability (NBTI) is the major contributor to transistor aging which affect PMOS transistors. The input vectors applied to the logic core has a significant impact on the overall aging of the logic block. In this paper, we present an efficient input vector selection technique based on Linear Programming (LP) to be used for maximum relaxation during the standby phase. We consider an accurate delay model for post-aging critical paths. Our mixed-LP (binary-relaxed) formulation scales well for very large circuits and provides near-optimal solutions. Experimental results and comparison with Monte-Carlo simulations show the speedup (4-5 orders of magnitude) and further optimization (11%) of our approach. Using these input vectors for the standby phase, the aging effect can be postponed by 71% in average.

Power supply noise in nano-scale VLSI is one of the design concerns. Due to switching current of various logic gates, the actual supply voltage seen by different devices fluctuates, causing extra delays and ultimately intermittent faults during operation. Therefore, accurate estimation of worst case scenario, maximum noise and the vectors causing it, is extremely important for design, verification, and manufacturing test steps. In this paper we present a mixed-integer linear programming modeling of power supply noise in digital circuits to obtain fast and accurate solutions. Compared with accurate SPICE simulations of random vectors for a set of benchmark circuits, the proposed approach can achieve 13115× speedup while obtains 2.7% more optimization in average.