A cache consolidation design of MLC STT-RAM for energy efficiency enhancement on cyber-physical systems

2021 ◽  
Vol 21 (1) ◽  
pp. 37-49
Author(s):  
Yu-Pei Liang ◽  
Shuo-Han Chen ◽  
Yuan-Hao Chang ◽  
Yun-Fei Liu ◽  
Hsin-Wen Wei ◽  
...  

Owing to the energy-constraint nature of cyber-physical systems (CPS), energy efficiency has become a primary design consideration for CPS. On CPS, owing to the high leakage power issue of SRAM, the major portion of its energy consumption comes from static random-access memory (SRAM)-based processors. Recently, with the emerging and rapidly evolving nonvolatile Spin-Transfer Torque RAM (STT-RAM), STT-RAM is expected to replace SRAM within processors for enhancing the energy efficiency with its near-zero leakage power features. The advances in Magnetic Tunneling Junction (MTJ) technology also realize the multi-level cell (MLC) STT-RAM to pack more cells with the same die area for achieving the memory density. However, the write disturbance issue of MLC STT-RAM prevents STT-RAM from properly resolving the energy efficiency of CPS. Although studies have been proposed to alleviate this issue, previous strategies could induce additional management overhead due to the use of counters or lead to frequent swap operations. Such an observation motivates us to propose an effective and simple strategy to combine direct and split cache mapping designs to enhance the energy efficiency of MLC STT-RAM. A series of experiments have been conducted on an open-source emulator with encouraging results.

2021 ◽  
Vol 26 (3) ◽  
pp. 1-17
Author(s):  
Urmimala Roy ◽  
Tanmoy Pramanik ◽  
Subhendu Roy ◽  
Avhishek Chatterjee ◽  
Leonard F. Register ◽  
...  

We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼500 hours of computation. On the other hand, if 10 6 samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken ∼250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.


2012 ◽  
Vol 48 (11) ◽  
pp. 3025-3030 ◽  
Author(s):  
E. Chen ◽  
D. Apalkov ◽  
A. Driskill-Smith ◽  
A. Khvalkovskiy ◽  
D. Lottis ◽  
...  

SPIN ◽  
2012 ◽  
Vol 02 (03) ◽  
pp. 1240001 ◽  
Author(s):  
ZIHUI WANG ◽  
YUCHEN ZHOU ◽  
JING ZHANG ◽  
YIMING HUAI

This paper reviews the recent progress made to realize reliable write operations in spin transfer torque magnetic random access memory. Theoretical description of write error rate (WER) based on macro-spin models are discussed with comparison to experimental data. Recent studies on the phenomena that can lead to abnormal WER behaviors which include back-hopping and low probability bifurcated switching are reviewed with emphasis on underlying mechanism. The studies on the WER in perpendicular magnetic tunnel junction (MTJ) are also reviewed. It is demonstrated that, for both in-plane and perpendicular MTJ, reliable and error-free write operations can be achieved with thorough understanding of the underlying physics and innovative design/process solutions.


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