Fine-grained dynamic voltage and frequency scaling for precise energy and performance trade-off based on the ratio of off-chip access to on-chip computation times

2004 ◽  
Author(s):  
Kihwan Choi ◽  
R. Soma ◽  
M. Pedram
Keyword(s):  
Frequency Scaling ◽  
Trade Off ◽  
Fine Grained ◽  
Dynamic Voltage ◽  
And Performance ◽  
On Chip

scholarly journals On-Chip Intelligent Dynamic Frequency Scaling for Real-Time Systems

2020 ◽  
Author(s):  
Prachi Sharma ◽  
Arkid Bera ◽  
Anu Gupta
Keyword(s):  
Real Time ◽  
User Satisfaction ◽  
Frequency Scaling ◽  
Power Efficient ◽  
Suggested Technique ◽  
Dynamic Voltage ◽  
Memory Accesses ◽  
And Performance ◽  
On Chip ◽  
Time Systems

<div> <div> <div> <p>To curb redundant power consumption in portable embedded and real-time applications, processors are equipped with various Dynamic Voltage and Frequency Scaling (DVFS) techniques. The accuracy of the prediction of the operating frequency of any such technique determines how power-efficient it makes a processor for a variety of programs and users. But, in the recent techniques, the focus has been too much on saving power, thus, ignoring the user-satisfaction metric, i.e. performance. The DVFS technique used to save power, in turn, introduces unwanted latency due to the high complexity of the algorithm. Also, many of the modern DVFS techniques provide feedback manually triggered by the user to change the frequency to conserve energy efficiently, thus, further increasing the reaction time. In this paper, we imple- ment a novel Artificial Neural Networks-driven frequency scaling methodology, which makes it possible to save power and boost performance at the same time, implicitly i.e. without any feedback from the user. Also, to make the system more inclusive concerning the kinds of processes run on it, we trained the ANN not only for CPU-intensive programs but also on the ones that are more memory-bound, i.e. which have frequent memory accesses during its average CPU cycle. The proposed technique has been evaluated on Intel i7-4720HQ Haswell processor and has shown performance boost by up to 20%, SoC power savings up to 16%, and Performance per Watt improvement by up to 30%, as compared to the existing DVFS technique. An open-source memory-intensive benchmark kit called Mibench was used to verify the utility of the suggested technique. </p> </div> </div> </div>

scholarly journals On-Chip Intelligent Dynamic Frequency Scaling for Real-Time Systems

2020 ◽  
Author(s):  
Prachi Sharma ◽  
Arkid Bera ◽  
Anu Gupta
Keyword(s):  
Real Time ◽  
User Satisfaction ◽  
Frequency Scaling ◽  
Power Efficient ◽  
Suggested Technique ◽  
Dynamic Voltage ◽  
Memory Accesses ◽  
And Performance ◽  
On Chip ◽  
Time Systems

<div> <div> <div> <p>To curb redundant power consumption in portable embedded and real-time applications, processors are equipped with various Dynamic Voltage and Frequency Scaling (DVFS) techniques. The accuracy of the prediction of the operating frequency of any such technique determines how power-efficient it makes a processor for a variety of programs and users. But, in the recent techniques, the focus has been too much on saving power, thus, ignoring the user-satisfaction metric, i.e. performance. The DVFS technique used to save power, in turn, introduces unwanted latency due to the high complexity of the algorithm. Also, many of the modern DVFS techniques provide feedback manually triggered by the user to change the frequency to conserve energy efficiently, thus, further increasing the reaction time. In this paper, we imple- ment a novel Artificial Neural Networks-driven frequency scaling methodology, which makes it possible to save power and boost performance at the same time, implicitly i.e. without any feedback from the user. Also, to make the system more inclusive concerning the kinds of processes run on it, we trained the ANN not only for CPU-intensive programs but also on the ones that are more memory-bound, i.e. which have frequent memory accesses during its average CPU cycle. The proposed technique has been evaluated on Intel i7-4720HQ Haswell processor and has shown performance boost by up to 20%, SoC power savings up to 16%, and Performance per Watt improvement by up to 30%, as compared to the existing DVFS technique. An open-source memory-intensive benchmark kit called Mibench was used to verify the utility of the suggested technique. </p> </div> </div> </div>

scholarly journals Low-Process–Voltage–Temperature-Sensitivity Multi-Stage Timing Monitor for System-on-Chip Applications

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1587
Author(s):  
Duo Sheng ◽  
Hsueh-Ru Lin ◽  
Li Tai
Keyword(s):  
High Performance ◽  
Power Reduction ◽  
System On Chip ◽  
Timing Information ◽  
Multi Stage ◽  
Dynamic Voltage ◽  
And Performance ◽  
On Chip ◽  
Maximum Measurement ◽  
Maximum Measurement Error

High performance and complex system-on-chip (SoC) design require a throughput and stable timing monitor to reduce the impacts of uncertain timing and implement the dynamic voltage and frequency scaling (DVFS) scheme for overall power reduction. This paper presents a multi-stage timing monitor, combining three timing-monitoring stages to achieve a high timing-monitoring resolution and a wide timing-monitoring range simultaneously. Additionally, because the proposed timing monitor has high immunity to the process–voltage–temperature (PVT) variation, it provides a more stable time-monitoring results. The time-monitoring resolution and range of the proposed timing monitor are 47 ps and 2.2 µs, respectively, and the maximum measurement error is 0.06%. Therefore, the proposed multi-stage timing monitor provides not only the timing information of the specified signals to maintain the functionality and performance of the SoC, but also makes the operation of the DVFS scheme more efficient and accurate in SoC design.

scholarly journals Exploiting Memory Resilience for Emerging Technologies: An Energy-Aware Resilience Exemplar for STT-RAM Memories

2020 ◽  
pp. 505-526
Author(s):  
Amir Mahdi Hosseini Monazzah ◽  
Amir M. Rahmani ◽  
Antonio Miele ◽  
Nikil Dutt
Keyword(s):  
Energy Consumption ◽  
Error Rate ◽  
Spin Transfer Torque ◽  
Error Correction Codes ◽  
Energy Aware ◽  
Trade Off ◽  
Protection Scheme ◽  
And Performance ◽  
On Chip ◽  
Rate Threshold

AbstractDue to the consistent pressing quest of larger on-chip memories and caches of multicore and manycore architectures, Spin Transfer Torque Magnetic RAM (STT-MRAM or STT-RAM) has been proposed as a promising technology to replace classical SRAMs in near-future devices. Main advantages of STT-RAMs are a considerably higher transistor density and a negligible leakage power compared with SRAM technology. However, the drawback of this technology is the high probability of errors occurring especially in write operations. Such errors are asymmetric and transition-dependent, where 0 → 1 is the most critical one, and is high subjected to the amount and current (voltage) supplied to the memory during the write operation. As a consequence, STT-RAMs present an intrinsic trade-off between energy consumption vs. reliability that needs to be properly tuned w.r.t. the currently running application and its reliability requirement. This chapter proposes FlexRel, an energy-aware reliability improvement architectural scheme for STT-RAM cache memories. FlexRel considers a memory architecture provided with Error Correction Codes (ECCs) and a custom current regulator for the various cache ways and conducts a trade-off between reliability and energy consumption. FlexRel cache controller dynamically profiles the number of 0 → 1 transitions of each individual bit write operation in a cache block and based on that selects the most-suitable cache way and current level to guarantee the necessary error rate threshold (in terms of occurred write errors) while minimizing the energy consumption. We experimentally evaluated the efficiency of FlexRel against the most efficient uniform protection scheme from reliability, energy, area, and performance perspectives. Experimental simulations performed by using gem5 has demonstrated that while FlexRel satisfies the given error rate threshold, it delivers up to 13.2% energy saving. From the area footprint perspective, FlexRel delivers up to 7.9% cache ways’ area saving. Furthermore, the performance overhead of the FlexRel algorithm which changes the traffic patterns of the cache ways during the executions is 1.7%, on average.

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