A Defect-Aware Reconfigurable Cache Architecture for Low-Vccmin DVFS-Enabled Systems

Mavropoulos, Michail; Κεραμίδας, Γεώργιος; Nikolos, D.

doi:10.7873/date.2015.1068

Cited by 7 publications

(3 citation statements)

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“…The work in [14] addresses the latter issue by combining the functionality of a typical spare cache [20] with the functionality of a victim cache [10]. Another recent work [13], called DARCA, presents an alternative usage of the redundant memory space. Instead of using the redundant elements for hosting the data and tag parts of the defective blocks (databased redundancy), DARCA devotes the extra storage to hold information (extra tag and status bits) to control how the cache data array stores and disambiguates the cached data (controlbased redundancy).…”

Section: Related Workmentioning

confidence: 99%

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Run Time Management of Faulty Data Caches

Mavropoulos

Κεραμίδας

Nikolos

2021

2021 IEEE European Test Symposium (ETS)

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As the technology continuous to shrink, power consumption appears to be the main design parameter. Operation on low voltage negatively affects mainly the operation of on-chip memories, resulting in multiple malfunctioning memory cells. As a reaction many cache fault tolerance (CFT) mechanisms have been proposed targeting the mitigation of performance degradation. The challenge is to devise mechanisms that are tailored to the memory access patterns of the executing applications.In this work we initially investigate the impact of the granularity of cache line disabling scheme in the first level data caches. Based on our analysis, we propose a run time adaptive mechanism that is able to opt the cache (sub-)block taking into account the diverse memory characteristics of the application. The proposed mechanism is based on the widely used block (sub-block) disabling scheme, and dynamically selects the appropriate sub-block granularity during the execution of the applications. Our evaluation results reveal that the proposed dynamic approach is able to offer significant benefits over a faulty cache design with a monolithic (sub-)block granularity.

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Section: Related Workmentioning

confidence: 99%

“…Therefore, it becomes critical to investigate new CFT techniques [13][14] [17] [22]. Obviously, these techniques have to be both lightweight and performance effective, especially when the target caches are close to the core (e.g., L1 caches).…”

Section: Introductionmentioning

confidence: 99%

Run Time Management of Faulty Data Caches

Mavropoulos

Κεραμίδας

Nikolos

2021

2021 IEEE European Test Symposium (ETS)

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“…However, a proper cache switching modelling is required to utilize this technique in real time. In [13], an efficient cache architecture is presented for DVFS-enabled devices top reduce the cache overhead in the cloud computing environment. This technique is capable of handling faults by changing associativity adaptively in the network.…”

Section: Related Workmentioning

confidence: 99%

Cache optimization cloud scheduling (COCS) algorithm based on last level caches

Kumar¹,

Ranvijay²

2019

IJAAS

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<p><span>Recently, the utilization of cloud services like storage, various software, networking resources has extremely enhanced due to widespread demand of these cloud services all over the world. On the other hand, it requires huge amount of storage and resource management to accurately cope up with ever-increasing demand. The high demand of these cloud services can lead to high amount of energy consumption in these cloud centers. Therefore, to eliminate these drawbacks and improve energy consumption and storage enhancement in real time for cloud computing devices, we have presented Cache Optimization Cloud Scheduling (COCS) Algorithm Based on Last Level Caches to ensure high cache memory Optimization and to enhance the processing speed of I/O subsystem in a cloud computing environment which rely upon Dynamic Voltage and Frequency Scaling (DVFS). The proposed COCS technique helps to reduce last level cache failures and the latencies of average memory in cloud computing multi-processor devices. This proposed COCS technique provides an efficient mathematical modelling to minimize energy consumption. We have tested our experiment on Cybershake scientific dataset and the experimental results are compared with different conventional techniques in terms of time taken to accomplish task, power consumed in the VMs and average power required to handle tasks.</span></p>

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