Statistical leakage estimation in 32nm CMOS considering cells correlations

International audienceIn this paper a method to estimate the leakage power consumption of CMOS digital circuits taking into account input states and process variations is proposed. The statistical leakage estimation is based on a pre-characterization of library cells considering correlations (ρ) between cells leakages. A method to create cells leakage correlation matrix is introduced. The maximum relative error achieved in the correlation matrix is 0.4% with respect to the correlations obtained by Monte Carlo simulations. Next the total circuit leakage is calculated from this matrix and cells leakage means and variances. The accuracy and efficiency of the approach is demonstrated on a C3540 (8 bit ALU) ISCAS85 Benchmark circuit

On Mitigation of Side-Channel Attacks in 3D ICs: Decorrelating Thermal Patterns from Power and Activity

Various side-channel attacks (SCAs) on ICs have been successfully demonstrated and also mitigated to some degree. In the context of 3D ICs, however, prior art has mainly focused on efficient implementations of classical SCA countermeasures. That is, SCAs tailored for up-and-coming 3D ICs have been overlooked so far. In this paper, we conduct such a novel study and focus on one of the most accessible and critical side channels: thermal leakage of activity and power patterns. We address the thermal leakage in 3D ICs early on during floorplanning, along with tailored extensions for power and thermal management. Our key idea is to carefully exploit the specifics of material and structural properties in 3D ICs, thereby decorrelating the thermal behaviour from underlying power and activity patterns. Most importantly, we discuss powerful SCAs and demonstrate how our open-source tool helps to mitigate them.Comment: Published in Proc. Design Automation Conference, 201

VarSim: A Fast Process Variation-aware Thermal Modeling Methodology Using Green's Functions

Despite temperature rise being a first-order design constraint, traditional thermal estimation techniques have severe limitations in modeling critical aspects affecting the temperature in modern-day chips. Existing thermal modeling techniques often ignore the effects of parameter variation, which can lead to significant errors. Such methods also ignore the dependence of conductivity on temperature and its variation. Leakage power is also incorporated inadequately by state-of-the-art techniques. Thermal modeling is a process that has to be repeated at least thousands of times in the design cycle, and hence speed is of utmost importance. To overcome these limitations, we propose VarSim, an ultrafast thermal simulator based on Green's functions. Green's functions have been shown to be faster than the traditional finite difference and finite element-based approaches but have rarely been employed in thermal modeling. Hence we propose a new Green's function-based method to capture the effects of leakage power as well as process variation analytically. We provide a closed-form solution for the Green's function considering the effects of variation on the process, temperature, and thermal conductivity. In addition, we propose a novel way of dealing with the anisotropicity introduced by process variation by splitting the Green's functions into shift-variant and shift-invariant components. Since our solutions are analytical expressions, we were able to obtain speedups that were several orders of magnitude over and above state-of-the-art proposals with a mean absolute error limited to 4% for a wide range of test cases. Furthermore, our method accurately captures the steady-state as well as the transient variation in temperature.Comment: 15 page

Hybrid Gate-Level Leakage Model for Monte Carlo Analysis on Multiple GPUs

This paper proposes a hybrid gate-level leakage model for the use with the Monte Carlo (MC) analysis approach, which combines a lookup table (LUT) model with a first-order exponential-polynomial model (first-order model, herein). For the process parameters having strong nonlinear relationships with the logarithm of leakage current, the proposed model uses the LUT approach for the sake of modeling accuracy. For the other process parameters, it uses the first-order model for increased efficiency. During the library characterization for each type of logic gates, the proposed approach determines the process parameters for which it will use the LUT model. And, it determines the number of LUT data points, which can maximize analysis efficiency with acceptable accuracy, based on the user-defined threshold. The proposed model was implemented for gate-level MC leakage analysis using three graphic processing units. In experiments, the proposed approach exhibited the average errors of <5% in both mean and standard deviation with reference to SPICE-level MC leakage analysis. In comparison, MC analysis with the first-order model exhibited more than 90% errors. In CPU times, the proposed hybrid approach took only two to five times longer runtimes. In comparison with the full LUT model, the proposed hybrid model was up to one hundred times faster while increasing the average errors by only 3%. Finally, the proposed approach completed a leakage analysis of an OpenSparc T2 core of 4.5 million gates with a runtime of <5 min.1150Ysciescopu

Modeling the Temperature Bias of Power Consumption for Nanometer-Scale CPUs in Application Processors

We introduce and experimentally validate a new macro-level model of the CPU temperature/power relationship within nanometer-scale application processors or system-on-chips. By adopting a holistic view, this model is able to take into account many of the physical effects that occur within such systems. Together with two algorithms described in the paper, our results can be used, for instance by engineers designing power or thermal management units, to cancel the temperature-induced bias on power measurements. This will help them gather temperature-neutral power data while running multiple instance of their benchmarks. Also power requirements and system failure rates can be decreased by controlling the CPU's thermal behavior. Even though it is usually assumed that the temperature/power relationship is exponentially related, there is however a lack of publicly available physical temperature/power measurements to back up this assumption, something our paper corrects. Via measurements on two pertinent platforms sporting nanometer-scale application processors, we show that the power/temperature relationship is indeed very likely exponential over a 20{\deg}C to 85{\deg}C temperature range. Our data suggest that, for application processors operating between 20{\deg}C and 50{\deg}C, a quadratic model is still accurate and a linear approximation is acceptable.Comment: Submitted to SAMOS 2014; International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV

Architectural level delay and leakage power modelling of manufacturing process variation

PhD ThesisThe effect of manufacturing process variations has become a major issue regarding the estimation of circuit delay and power dissipation, and will gain more importance in the future as device scaling continues in order to satisfy market place demands for circuits with greater performance and functionality per unit area. Statistical modelling and analysis approaches have been widely used to reflect the effects of a variety of variational process parameters on system performance factor which will be described as probability density functions (PDFs). At present most of the investigations into statistical models has been limited to small circuits such as a logic gate. However, the massive size of present day electronic systems precludes the use of design techniques which consider a system to comprise these basic gates, as this level of design is very inefficient and error prone. This thesis proposes a methodology to bring the effects of process variation from transistor level up to architectural level in terms of circuit delay and leakage power dissipation. Using a first order canonical model and statistical analysis approach, a statistical cell library has been built which comprises not only the basic gate cell models, but also more complex functional blocks such as registers, FIFOs, counters, ALUs etc. Furthermore, other sensitive factors to the overall system performance, such as input signal slope, output load capacitance, different signal switching cases and transition types are also taken into account for each cell in the library, which makes it adaptive to an incremental circuit design. The proposed methodology enables an efficient analysis of process variation effects on system performance with significantly reduced computation time compared to the Monte Carlo simulation approach. As a demonstration vehicle for this technique, the delay and leakage power distributions of a 2-stage asynchronous micropipeline circuit has been simulated using this cell library. The experimental results show that the proposed method can predict the delay and leakage power distribution with less than 5% error and at least 50,000 times faster computation time compare to 5000-sample SPICE based Monte Carlo simulation. The methodology presented here for modelling process variability plays a significant role in Design for Manufacturability (DFM) by quantifying the direct impact of process variations on system performance. The advantages of being able to undertake this analysis at a high level of abstraction and thus early in the design cycle are two fold. First, if the predicted effects of process variation render the circuit performance to be outwith specification, design modifications can be readily incorporated to rectify the situation. Second, knowing what the acceptable limits of process variation are to maintain design performance within its specification, informed choices can be made regarding the implementation technology and manufacturer selected to fabricate the design

정적 램 및 파워 게이트 회로에 대한 전압 및 보존용 공간 할당 문제

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 김태환.칩의 저전력 동작은 중요한 문제이며, 공정이 발전하면서 그 중요성은 점점 커지고 있다. 본 논문은 칩을 구성하는 정적 램(SRAM) 및 로직(logic) 각각에 대해서 저전력으로 동작시키는 방법론을 논한다. 우선, 본 논문에서는 칩을 문턱 전압 근처의 전압(NTV)에서 동작시키고자 할 때 모니터링 회로의 측정을 통해 칩 내의 모든 SRAM 블록에서 동작 실패가 발생하지 않는 최소 동작 전압을 추론하는 방법론을 제안한다. 칩을 NTV 영역에서 동작시키는 것은 에너지 효율성을 증대시킬 수 있는 매우 효과적인 방법 중 하나이지만 SRAM의 경우 동작 실패 때문에 동작 전압을 낮추기 어렵다. 하지만 칩마다 영향을 받는 공정 변이가 다르므로 최소 동작 전압은 칩마다 다르며, 모니터링을 통해 이를 추론해낼 수 있다면 칩별로 SRAM에 서로 다른 전압을 인가해 에너지 효율성을 높일 수 있다. 본 논문에서는 다음과 같은 과정을 통해 이 문제를 해결한다: (1) 디자인 인프라 설계 단계에서는 SRAM의 최소 동작 전압을 추론하고 칩 생산 단계에서는 SRAM 모니터의 측정을 통해 전압을 인가하는 방법론을 제안한다; (2) 칩의 SRAM 비트셀(bitcell)과 주변 회로를 포함한 SRAM 블록들의 공정 변이를 모니터링할 수 있는 SRAM 모니터와 SRAM 모니터에서 모니터링할 대상을 정의한다; (3) SRAM 모니터의 측정값을 이용해 같은 칩에 존재하는 모든 SRAM 블록에서 목표 신뢰수준 내에서 읽기, 쓰기, 및 접근 동작 실패가 발생하지 않는 최소 동작 전압을 추론한다. 벤치마크 회로의 실험 결과는 본 논문에서 제안한 방법을 따라 칩별로 SRAM 블록들의 최소 동작 전압을 다르게 인가할 경우, 기존 방법대로 모든 칩에 동일한 전압을 인가하는 것 대비 수율은 같은 수준으로 유지하면서 SRAM 비트셀 배열의 전력 소모를 감소시킬 수 있음을 보인다. 두 번째로, 본 논문에서는 파워 게이트 회로에서 기존의 보존용 공간 할당 방법들이 지니고 있는 문제를 해결하고 누설 전력 소모를 더 줄일 수 있는 방법론을 제안한다. 기존의 보존용 공간 할당 방법은 멀티플렉서 피드백 루프가 있는 모든 플립플롭에는 무조건 보존용 공간을 할당해야 해야 하기 때문에 다중 비트 보존용 공간의 장점을 충분히 살리지 못하는 문제가 있다. 본 논문에서는 다음과 같은 방법을 통해 보존용 공간을 최소화하는 문제를 해결한다: (1) 보존용 공간 할당 과정에서 멀티플렉서 피드백 루프를 무시할 수 있는 조건을 제시하고, (2) 해당 조건을 이용해 멀티플렉서 피드백 루프가 있는 플립플롭이 많이 존재하는 회로에서 보존용 공간을 최소화한다; (3) 추가로, 플립플롭에 이미 할당된 보존용 공간 중 일부를 제거할 수 있는 조건을 찾고, 이를 이용해 보존용 공간을 더 감소시킨다. 벤치마크 회로의 실험 결과는 본 논문에서 제안한 방법론이 기존의 보존용 공간 할당 방법론보다 더 적은 보존용 공간을 할당하며, 따라서 칩의 면적 및 전력 소모를 감소시킬 수 있음을 보인다.Low power operation of a chip is an important issue, and its importance is increasing as the process technology advances. This dissertation addresses the methodology of operating at low power for each of the SRAM and logic constituting the chip. Firstly, we propose a methodology to infer the minimum operating voltage at which SRAM failure does not occur in all SRAM blocks in the chip operating on near threshold voltage (NTV) regime through the measurement of a monitoring circuit. Operating the chip on NTV regime is one of the most effective ways to increase energy efficiency, but in case of SRAM, it is difficult to lower the operating voltage because of SRAM failure. However, since the process variation on each chip is different, the minimum operating voltage is also different for each chip. If it is possible to infer the minimum operating voltage of SRAM blocks of each chip through monitoring, energy efficiency can be increased by applying different voltage. In this dissertation, we propose a new methodology of resolving this problem. Specifically, (1) we propose to infer minimum operation voltage of SRAM in design infra development phase, and assign the voltage using measurement of SRAM monitor in silicon production phase; (2) we define a SRAM monitor and features to be monitored that can monitor process variation on SRAM blocks including SRAM bitcell and peripheral circuits; (3) we propose a new methodology of inferring minimum operating voltage of SRAM blocks in a chip that does not cause read, write, and access failures under a target confidence level. Through experiments with benchmark circuits, it is confirmed that applying different voltage to SRAM blocks in each chip that inferred by our proposed methodology can save overall power consumption of SRAM bitcell array compared to applying same voltage to SRAM blocks in all chips, while meeting the same yield target. Secondly, we propose a methodology to resolve the problem of the conventional retention storage allocation methods and thereby further reduce leakage power consumption of power gated circuit. Conventional retention storage allocation methods have problem of not fully utilizing the advantage of multi-bit retention storage because of the unavoidable allocation of retention storage on flip-flops with mux-feedback loop. In this dissertation, we propose a new methodology of breaking the bottleneck of minimizing the state retention storage. Specifically, (1) we find a condition that mux-feedback loop can be disregarded during the retention storage allocation; (2) utilizing the condition, we minimize the retention storage of circuits that contain many flip-flops with mux-feedback loop; (3) we find a condition to remove some of the retention storage already allocated to each of flip-flops and propose to further reduce the retention storage. Through experiments with benchmark circuits, it is confirmed that our proposed methodology allocates less retention storage compared to the state-of-the-art methods, occupying less cell area and consuming less power.1 Introduction 1 1.1 Low Voltage SRAM Monitoring Methodology 1 1.2 Retention Storage Allocation on Power Gated Circuit 5 1.3 Contributions of this Dissertation 8 2 SRAM On-Chip Monitoring Methodology for High Yield and Energy Efficient Memory Operation at Near Threshold Voltage 13 2.1 SRAM Failures 13 2.1.1 Read Failure 13 2.1.2 Write Failure 15 2.1.3 Access Failure 16 2.1.4 Hold Failure 16 2.2 SRAM On-chip Monitoring Methodology: Bitcell Variation 18 2.2.1 Overall Flow 18 2.2.2 SRAM Monitor and Monitoring Target 18 2.2.3 Vfail to Vddmin Inference 22 2.3 SRAM On-chip Monitoring Methodology: Peripheral Circuit IR Drop and Variation 29 2.3.1 Consideration of IR Drop 29 2.3.2 Consideration of Peripheral Circuit Variation 30 2.3.3 Vddmin Prediction including Access Failure Prohibition 33 2.4 Experimental Results 41 2.4.1 Vddmin Considering Read and Write Failures 42 2.4.2 Vddmin Considering Read/Write and Access Failures 45 2.4.3 Observation for Practical Use 45 3 Allocation of Always-On State Retention Storage for Power Gated Circuits - Steady State Driven Approach 49 3.1 Motivations and Analysis 49 3.1.1 Impact of Self-loop on Power Gating 49 3.1.2 Circuit Behavior Before Sleeping 52 3.1.3 Wakeup Latency vs. Retention Storage 54 3.2 Steady State Driven Retention Storage Allocation 56 3.2.1 Extracting Steady State Self-loop FFs 57 3.2.2 Allocating State Retention Storage 59 3.2.3 Designing and Optimizing Steady State Monitoring Logic 59 3.2.4 Analysis of the Impact of Steady State Monitoring Time on the Standby Power 63 3.3 Retention Storage Refinement Utilizing Steadiness 65 3.3.1 Extracting Flip-flops for Retention Storage Refinement 66 3.3.2 Designing State Monitoring Logic and Control Signals 68 3.4 Experimental Results 73 3.4.1 Comparison of State Retention Storage 75 3.4.2 Comparison of Power Consumption 79 3.4.3 Impact on Circuit Performance 82 3.4.4 Support for Immediate Power Gating 83 4 Conclusions 89 4.1 Chapter 2 89 4.2 Chapter 3 90박