Date: Wednesday 11 March 2020
Time: 17:00 - 18:30
Location / Room: Lesdiguières
Chair:
Michail Maniatakos, New York University Abu Dhabi, UA
Co-Chair:
Alessandro Savino, Politecnico di Torino, IT
Reliability analysis and protection at the microarchitecture level is of paramount importance to speed-up the design face of any computing system. On the analysis side, this session starts presenting a reverse-order ACE (Architecturally Correct Execution) analysis that is more accurate than original ACE proposals, then moving to an instruction level analysis based on a genetic-algorithm able to improve program resiliency to errors. Finally, on the protection side, the session presents a low-cost ECC plus approximation mechanism for GPU register files.
| Time | Label | Presentation Title Authors |
|---|---|---|
| 17:00 | 8.6.1 | RACE: REVERSE-ORDER PROCESSOR RELIABILITY ANALYSIS Authors: Athanasios Chatzidimitriou and Dimitris Gizopoulos, University of Athens, GR Abstract Modern microprocessors suffer from increased error rates that come along with fabrication technology scaling. Processor designs continuously become more prone to hardware faults that lead to execution errors and system failures, which raise the requirement of protection mechanisms. However, error mitigation strategies have to be applied diligently, as they impose significant power, area, and performance overheads. Early and accurate reliability estimation of a microprocessor design is essential in order to determine the most vulnerable hardware structures and the most efficient protection schemes. One of the most commonly used techniques for reliability estimation is Architecturally Correct Execution (ACE) analysis. ACE analysis can be applied at different abstraction models, including microarchitecture and RTL and often requires a single or few simulations to report the Architectural Vulnerability Factor (AVF) of the processor structures. However, ACE analysis overestimates the vulnerability of structures because of its pessimistic, worst-case nature. Moreover, it only delivers coarse-grain vulnerability reports and no details about the expected result of hardware faults (silent data corruptions, crashes). In this paper, we present reverse ACE (rACE), a methodology that (a) improves the accuracy of ACE analysis and (b) delivers fine-grain error outcome reports. Using a reverse-order tracing flow, rACE analysis associates portions of the simulated execution of a program with the actual output and the control flow, delivering finer accuracy and results classification. Our findings show that rACE reports an average 1.45X overestimation, compared to Statistical Fault Injection, for different sizes of the register file of an out-of-order CPU core (executing both ARM and x86 binaries), when a baseline ACE analysis reports 2.3X overestimation and even refined versions of ACE analysis report an average of 1.8X overestimation. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 17:30 | 8.6.2 | DEFCON: GENERATING AND DETECTING FAILURE-PRONE INSTRUCTION SEQUENCES VIA STOCHASTIC SEARCH Speaker: Ioannis Tsiokanos, Queen's University Belfast, GB Authors: Ioannis Tsiokanos1, Lev Mukhanov1, Giorgis Georgakoudis2, Dimitrios S. Nikolopoulos3 and Georgios Karakonstantis1 1Queen's University Belfast, GB; 2Lawrence Livermore National Laboratory, US; 3Virginia Tech, US Abstract The increased variability and adopted low supply voltages render nanometer devices prone to timing failures, which threaten the functionality of digital circuits. Recent schemes focused on developing instruction-aware failure prediction models and adapting voltage/frequency to avoid errors while saving energy. However, such schemes may be inaccurate when applied to pipelined cores since they consider only the currently executed instruction and the preceding one, thereby neglecting the impact of all the concurrently executing instructions on failure occurrence. In this paper, we first demonstrate that the order and type of instructions in sequences with a length equal to the pipeline depth affect significantly the failure rate. To overcome the practically impossible evaluation of the impact of all possible sequences on failures, we present DEFCON, a fully automated framework that stochastically searches for the most failure-prone instruction sequences (ISQs). DEFCON generates such sequences by integrating a properly formulated genetic algorithm with accurate post-layout dynamic timing analysis, considering the data-dependent path sensitization and instruction execution history. The generated micro-architecture aware ISQs are then used by DEFCON to estimate the failure vulnerability of any application. To evaluate the efficacy of the proposed framework, we implement a pipelined floating-point unit and perform dynamic timing analysis based on input data that we extract from a variety of applications consisting of up-to 43.5M ISQs. Our results show that DEFCON reveals quickly ISQs that maximize the output quality loss and correctly detects 99.7% of the actual faulty ISQs in different applications under various levels of variation-induced delay increase. Finally, DEFCON enable us to identify failure-prone ISQs early at the design cycle, and save 26.8% of energy on average when combined with a clock stretching mechanism. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 18:00 | 8.6.3 | LAD-ECC: ENERGY-EFFICIENT ECC MECHANISM FOR GPGPUS REGISTER FILE Speaker: Hengshan Yue, Jilin University, CN Authors: Xiaohui Wei, Hengshan Yue and Jingweijia Tan, Jilin University, CN Abstract Graphics Processing Units (GPUs) are widely used in general-purpose high-performance computing applications (i.e., GPGPUs), which require reliable execution in the presence of soft errors. To support massive thread level parallelism, a sizeable register file is adopted in GPUs, which is highly vulnerable to soft errors. Although modern commercial GPUs provide single-error-correction double-error-detection (SEC-DED) ECC for the register file, it consumes a considerable amount of energy due to frequent register accesses and leakage power of ECC storage. In this paper, we propose to Leverage Approximation and Duplication characteristics of register values to build an energy-efficient ECC mechanism (LAD-ECC) in GPGPUs, which consists of APproximation-aware ECC (AP-ECC) and Duplication-Aware ECC (DA-ECC). Leveraging the inherent error tolerance features, AP-ECC merely protects significant bits of registers to combat the critical error. Observing same-named registers across threads usually keep the same data, DA-ECC avoids unnecessary ECC generation and verification for duplicate register values. Experimental results demonstrate that our LAD-ECC tremendously reduces 69.72% energy consumption of traditional SEC-DED ECC. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 18:30 | IP4-9, 698 | EXPLFRAME: EXPLOITING PAGE FRAME CACHE FOR FAULT ANALYSIS OF BLOCK CIPHERS Speaker: Anirban Chakraborty, IIT Kharagpur, IN Authors: Anirban Chakraborty1, Sarani Bhattacharya2, Sayandeep Saha1 and Debdeep Mukhopadhyay1 1IIT Kharagpur, IN; 2KU Leuven, BE Abstract Page Frame Cache (PFC) is a purely software cache, present in modern Linux based operating systems (OS), which stores the page frames that were recently released by the processes running on a particular CPU. In this paper, we show that the page frame cache can be maliciously exploited by an adversary to steer the pages of a victim process to some pre-decided attacker-chosen locations in the memory. We practically demonstrate an end-to-end attack, emph{ExplFrame}, where an attacker having only user-level privilege is able to force a victim process's memory pages to vulnerable locations in DRAM and deterministically conduct Rowhammer to induce faults. As a case study, we induce single bit faults in the T-tables on OpenSSL (v1.1.1) AES using our proposed attack ExplFrame. We also propose an improvised fault analysis technique which can exploit any Rowhammer-induced bit-flips in the AES T-tables. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 18:30 | End of session | |