Cross-Layer Approaches for an Aging-Aware Design Space Exploration for Microprocessors

Abstract

-With the continuous scaling of CMOS technologies, maintaining the microprocessor reliability becomes a major design challenge. In particular, accelerated transistor aging is a serious reliability concern, as it considerably reduces the operational system lifetime. To address this issue, in this work cross-layer solutions for aging modeling, simulation and mitigation are proposed, to be able to co-optimize reliability together with the traditional design constraints such as power, performance, and cost. Therefore, the knowledge from several abstraction layers, ranging circuit- to architecture-level, are exploited for cost-effective aging-aware architecture and system design. The comprehensive simulations and experimental analysis performed in this work show the benefits of this approach over state-of-the-art single-layer solutions.

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Thanks to the aggressive scaling of transistor dimensions in the past decades, computing systems have revolutionized our life. However, in the shade of the downscaling benefits such as increased microprocessor performance, more integrated features and improved energy/cost efficiency, the reliability of nanoscale devices became a major threat for the future success of computing systems (see Fig. 1) [ 2 ]–[ 5 ]. As a result, with every new technology node, it becomes harder for chip manufacturers to ensure the reliable operation of their chips, and thus malfunctions during the operational mode, that can lead to erroneous program outputs or even system crashes, become more likely.

Among various reliability challenges, accelerated transistor aging is of particular importance, as it degrades the transistor switching speed, and thus leads to slower circuits over time [ 3 ], [ 6 ]. Consequently, in synchronous digital systems, timing failures due to the increased circuit delay can occur and cause incorrect system states. Because of that, the microprocessor lifetime and as a result also the overall system lifetime is considerably reduced, if no countermeasures are taken. This is especially critical for embedded systems that require long mission times, for instance in health care (e.g. implants), space missions (e.g. satellites) or electronic control units (e.g. in airplanes) [ 7 ]. Therefore, it is a necessity to consider reliability, and in particular lifetime, as another design constraint, beside the traditional performance, power and cost parameters. However, due to the strong interdependencies among these different criteria, the co-optimization is very challenging.

To avoid aging-induced failures, designers add conservative timing margins to their designs, which, however, is inefficient and costly [ 8 ].

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Aging acceleration in the next technology nodes (based on [ 2 ]) e t a R e r u l i a F

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1Cross-Layer means that the knowledge and parameters available at multiple abstraction layers are used in combination to optimize the design, whereas single-layer solutions exploit only the information of a single level. time techniques, which are incapable of dealing with runtime variations (e.g. changing system conditions). Because of that, the design time solutions consider lifetime as one optimization objective and tune the design accordingly based on a given set of representative workload scenarios, while the runtime technique deals with the constantly changing conditions and adapts the system to avoid critical states. Thus, the combination of both schemes enables effective and holistic aging mitigation solutions, which allow a very aggressive and cost-effective system design.

In the following, the different contributions are explained in detail. II. FRAMEWORKS FOR WEAROUT MODELING AND EVALUATING

The first framework developed, is an architectural platform called ExtraTime [ 14 ]. It is based on the performance simulator gem5 [ 15 ] which was extended with sophisticated models for power and temperature. In order to make these models as realistic as possible, they were optimized, and afterwards calibrated and validated using a real experimental platform based on recent Intel Core-i-processors, which have on-die power and thermal sensors [ 16 ]. As a result, the model accuracy is very good (e.g. temperature inaccuracy is < 2 C). In addition, novel and realistic architectural aging models were developed and incorporated (see Fig. 4). The main advantage of these models is that they do not require detailed circuit-level information to estimate the degradation of a complete architectural component (e.g. ALU, Branch Predictor, etc.). For this purpose, these models were derived from transistor-level models for BTI and HCI [ 17 ]– [ 19 ] by introducing a representative transistor, which reflects the average usage behavior (switching activity, ON time, OFF time) of all transistors within this block. In addition, the temperature of this representative transistor is estimated by the block temperature. By that means, the degradation of the representative transistor can be obtained, and thus the degradation of the entire block can be estimated. In this regard it is important to note that the accuracy of the resulting aging models is very good given their level of abstraction. In fact, the inaccuracy compared to accurate gate-level models for an architectural block such as an ALU is less than 5 % without requiring detailed circuit–level knowledge.

10 years 7 years 3 years 0 years 115 ] % [ in110 y a l e d ev105 i t a l e R100

As a result, this framework considers the influence of parameters at microarchitecture- up to application-level. Moreover, as ExtraTime does not require low-level details (e.g. the actual gate-level implementation), it can be employed in early design phases for a first-order aging analysis and design space exploration.

To also take low-level aspects into account, a second complementary platform was developed, which is based on standard EDA tools for design synthesis and simulation. It can analyze all internal gates, and it considers the interplay of real-world applications, aging, power and temperature [ 20 ]. Thus, it is very accurate as aging can be analyzed at gate-level using the models proposed in [ 17 ]–[ 19 ], but is less flexible compared to ExtraTime. Consequently, it is intended for fine tuning in later design phases.

III. DESIGN TIME & RUNTIME AGING MITIGATION SOLUTIONS

Using these novel cross-layer platforms the most critical microprocessor components were identified and several unique aging mitigation techniques were designed and evaluated, which are presented in the following subsections.

A. Aging-Aware Design of Instruction Pipelines

Traditionally the delays of all instruction pipeline stages are balanced at design time. However, transistor aging causes a nonuniform delay degradation among all stages due to different usage patterns (see Fig. 5(a)). Hence, this design approach results in an imbalanced and non-optimal design after a short period of time. Consequently, a single stage becomes the bottleneck for the overall processor lifetime. In other words, while one pipeline stage already produces timing failures, the other stages still operate correctly. To alleviate this problem, a novel instruction pipeline design paradigm is proposed (MTTF-balanced pipeline) according to which all stage delays are balanced at the end of the desired lifetime (see Fig. 5(b)). The main idea of this approach is to increase the timing slack of aging-critical stages to improve their lifetime, while the timing slack of non-critical stages can be reduced to improve their energy efficiency by using slower yet more energy efficient gates. As a result, the processor lifetime can be considerably improved by more than 2.3⇥ , and at the same time the power consumption can be reduced by 10 %. In addition, performance and cost are not affected [ 20 ]. This underlines, that it is much more effective to address aging already in early design phases, rather than only adding guardbands to the final design to cope with the delay degradation.

B. Aging-Aware Cross-Layer Instruction Scheduling

As shown in Fig. 5(a), the execution units belong to the most aging-critical processor components. Therefore, an aging-aware instruction scheduling technique was developed [ 21 ]. The novelty of this scheduling policy is that the timing-criticality of instructions (see Fig. 6) is considered during the scheduling process to reduce the load of units that execute critical instructions. Therefore, the 10 years 7 years 3 years 0 years 115 ] % [ in110 y a l e d ev105 i t a l e R100 95 Fetch PredecoDdeecodeRenamDeispatcIhssue RegReaEdxecuteLoad-SWtorrieteBRacektire 95 Fetch PredecoDdeecodeRenamDeispatcIhssue RegReaEdxecuteLoad-SWtorrieteBRacektire circuit-level delay of all instructions as well as their applicationlevel occurrence rates were analyzed to classify the instructions into timing-critical (those that start to fail first) and non-timing-critical instructions. Then, dedicated functional units are used for the different instruction classes. Since less than 20 % of all executed instructions are timing-critical, the unit(s) taking care of these instructions are idle most of the time, which is exploited to considerably improve the overall lifetime of the functional units2 by employing input vector control or aggressive power gating policies. In fact, our simulation results obtained with ExtraTime show that the overall lifetime can be improved by more than 1.6⇥ compared to existing scheduling policies that ignore the detailed timing information (i.e. these are single-layer solutions) and instead balance the number of incoming instructions among all available units.

C. Aging-Aware Instruction Set Encoding

Beside the execution units also the decoding stages of a microprocessor can become critical and limit the microprocessor lifetime (see Fig. 5(a)). Hence, the decoding stages require an aging-aware design. Since the instruction set encoding, i.e. the mapping between instructions and opcodes, has a strong influence on the wearout of the decoding stages (see Fig. 7), we propose a novel aging-aware instruction set encoding methodology called ArISE to address the delay degradation in the decoding stages [ 22 ]. This methodology exploits simulated annealing and genetic algorithms to optimize the instruction set encoding with respect to lifetime as well as power consumption, since exhaustive optimization solutions are infeasible due to the large number of possible encodings (i.e. typically more than 10200). The result is an optimization that yields significant lifetime improvements (more than 2⇥ compared to state-of-the-art) with negligible impact on other design parameters. This is due to the fact that power consumption and lifetime are co-optimized in our proposed approach which iteratively improves the encoding. If only 2Please note that still the units taking care of the timing-critical instructions limit the overall lifetime due to the way the instructions are classified.

Encoding 1 Encoding 2 Encoding 3

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Proactive Adaptation

one of these two parameters is considered, there will be considerable disadvantages for the other one as pointed out in [ 22 ]. D. Pro-Active Aging-Aware Dynamic Runtime Adaptation

To detect and avoid potentially critical conditions while the system is running, dynamic schemes employed at runtime have to complement solutions applied at design time [ 23 ]. However, the dynamic state-of-the-art techniques employ only reactive adaptation techniques. These are inefficient due to the nature of “damage control”-type of policies, i.e., they deal with already “aged” chips. In contrast, we propose a proactive and preventive runtime adaptation policy that tries to proactively slow down aging in all phases of the chip lifetime, and hence can prolong the lifetime more efficiently than the existing techniques (see Fig. 8), i.e. with lower performance and power overheads [ 24 ]. Therefore, an hierarchical expert system was developed (see Fig. 9) that takes input from a sensor network (or models running in software) to analyze the current system state as well as the trend of recent system states in a very fine-grained manner, i.e. every 1 ms-10 ms and adapts the system accordingly. Whenever a critical state or trend in terms of lifetime, temperature or power consumption is detected, the system is adapted by means of frequency and voltage tuning, that is, frequency and voltage are reduced by one level. If no parameter as well as no trend is critical, the current system performance is evaluated. If the performance can be maintained with a lower frequency level, frequency and supply voltage are lowered to improve lifetime, power consumption and temperature, otherwise the frequency is kept on the same level or is even increased if necessary. As a result, the lifetime of the entire microprocessor can be improved by more than 2⇥ , and the energy consumption can be reduced by 14 %, while the performance penalty is almost negligible (2 % on average). This shows that with such a cross-layer, proactive runtime adaptation technique the different design parameters can be co-optimized very effectively although the adaptation decisions are performed at system-level.

IV. SUMMARY

In this work cross-layer solutions for aging modeling and simulation as well as mitigation were pushed forward. Therefore, a set of unique frameworks and mitigation techniques were developed. In addition, it was demonstrated that cross-layer solutions allow a much more efficient co-optimization of all design parameters including lifetime compared to state-of-the-art single-layer solutions.

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0 Workshop on Early Reliability Modeling for Aging and Variability in Silicon Systems – March 18th 2016 – Co-Located with DATE 2016 Dresden, Germany Copyright © 2016 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

This work was partly supported by the German Research Foundation (DFG) as part of the national focal program “Dependable Embedded Systems” (SPP-1500, http://spp1500.ira.uka.de).

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