Dynamic Energy Management for Chip Multi-processors under Performance Constraints

We introduce a novel algorithm for dynamic energy management (DEM) under performance constraints in chip multi-processors (CMPs). Using the novel concept of delayed instructions count, performance loss estimations are calculated at the end of each control period for each core. In addition, a Kalman filtering based approach is employed to predict workload in the next control period for which voltage-frequency pairs must be selected. This selection is done with a novel dynamic voltage and frequency scaling (DVFS) algorithm whose objective is to reduce energy consumption but without degrading performance beyond the user set threshold. Using our customized Sniper based CMP system simulation framework, we demonstrate the effectiveness of the proposed algorithm for a variety of benchmarks for 16 core and 64 core network-on-chip based CMP architectures. Simulation results show consistent energy savings across the board. We present our work as an investigation of the tradeoff between the achievable energy reduction via DVFS when predictions are done using the effective Kalman filter for different performance penalty thresholds

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Dynamic Lifetime Reliability and Energy Management for Network-on-Chip based Chip Multiprocessors

In this dissertation, we study dynamic reliability management (DRM) and dynamic energy management (DEM) techniques for network-on-chip (NoC) based chip multiprocessors (CMPs). In the first part, the proposed DRM algorithm takes both the computational and the communication components of the CMP into consideration and combines thread migration and dynamic voltage and frequency scaling (DVFS) as the two primary techniques to change the CMP operation. The goal is to increase the lifetime reliability of the overall system to the desired target with minimal performance degradation. The simulation results on a variety of benchmarks on 16 and 64 core NoC based CMP architectures demonstrate that lifetime reliability can be improved by 100% for an average performance penalty of 7.7% and 8.7% for the two CMP architectures. In the second part of this dissertation, we first propose novel algorithms that employ Kalman filtering and long short term memory (LSTM) for workload prediction. These predictions are then used as the basis on which voltage/frequency (V/F) pairs are selected for each core by an effective dynamic voltage and frequency scaling algorithm whose objective is to reduce energy consumption but without degrading performance beyond the user set threshold. Secondly, we investigate the use of deep neural network (DNN) models for energy optimization under performance constraints in CMPs. The proposed algorithm is implemented in three phases. The first phase collects the training data by employing Kalman filtering for workload prediction and an efficient heuristic algorithm based on DVFS. The second phase represents the training process of the DNN model and in the last phase, the DNN model is used to directly identify V/F pairs that can achieve lower energy consumption without performance degradation beyond the acceptable threshold set by the user. Simulation results on 16 and 64 core NoC based architectures demonstrate that the proposed approach can achieve up to 55% energy reduction for 10% performance degradation constraints. Simulation experiments compare the proposed algorithm against existing approaches based on reinforcement learning and Kalman filtering and show that the proposed DNN technique provides average improvements in energy-delay-product (EDP) of 6.3% and 6% for the 16 core architecture and of 7.4% and 5.5% for the 64 core architecture

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Biomedical Applications of Zeolitic Nanoparticles, with an Emphasis on Medical Interventions

The advent of porous materials, in particular zeolitic nanoparticles, has opened up unprecedented putative research avenues in nanomedicine. Zeolites with intracrystal mesopores are low framework density aluminosilicates possessing a regular porous structure along with intricate channels. Their unique physiochemical as well as physiological parameters necessitate a comprehensive overview on their classifications, fabrication platforms, cellular/macromolecular interactions, and eventually their prospective biomedical applications through illustrating the challenges and opportunities in different integrative medical and pharmaceutical fields. More particularly, an update on recent advances in zeolite-accommodated drug delivery and the prevalent challenges regarding these molecular sieves is to be presented. In conclusion, strategies to accelerate the translation of these porous materials from bench to bedside along with common overlooked physiological and pharmacological factors of zeolite nanoparticles are discussed and debated. Furthermore, for zeolite nanoparticles, it is a matter of crucial importance, in terms of biosafety and nanotoxicology, to appreciate the zeolite-bio interface once the zeolite nanoparticles are exposed to the bio-macromolecules in biological media. We specifically shed light on interactions of zeolite nanoparticles with fibrinogen and amyloid beta which had been comprehensively investigated in our recent reports. Given the significance of zeolite nanoparticles’ interactions with serum or interstitial proteins conferring them new biological identity, the preliminary approaches for deeper understanding of administration, distribution, metabolism and excretion of zeolite nanoparticles are elucidated

Preclinical studies conducted on nanozyme antioxidants: Shortcomings and challenges based on US FDA regulations

The wide prevalence of oxidative stress-induced diseases has led to a growing demand for antioxidant therapeutics worldwide. Nanozyme antioxidants are drawing enormous attention as practical alternatives for conventional antioxidants. The considerable body of research over the last decade and the promising results achieved signify the potential of nanozyme antioxidants to secure a place in the expanding market of antioxidant therapeutics. Nonetheless, there is no report on clinical trials for their further evaluation. Through analyzing in-depth selected papers which have conducted in vivo studies on nanozyme antioxidants, this review aims to pinpoint and discuss possible reasons impeding development of research toward clinical studies and to offer some practical solutions for future studies to bridge the gap between preclinical and clinical stages. "We did not experience these kinds of strange illnesses in the past." Everybody might have heard such a familiar sentence from their grandparents and asked themselves, why? The current paper aims to provide readers with one of the answers: "Oxidative stress", which happens when the body fails to neutralize damage caused by unstable molecules called free radicals. In this paper, the authors present the seriousness of oxidative stress-induced clinical conditions. They discuss one of the promising treatments, nanozyme antioxidants, these are mostly based on nano-sized materials with enzyme-like function, in other words, they can speed up chemical reactions. Despite significant results, nanozyme antioxidants have not been investigated in clinical studies. This paper intends to search for the main reasons for this and suggest possible solutions. © 2021 Future Medicine Ltd

Recent progress in the intranasal PLGA-based drug delivery for neurodegenerative diseases treatment

One of the most challenging problems of the current treatments of neurodegenerative diseases is related to the permeation and access of most therapeutic agents to the central nervous system (CNS), prevented by the blood-brain barrier (BBB). Recently, intranasal (IN) delivery has opened new prospects because it directly delivers drugs for neurological diseases into the brain via the olfactory route. Recently, PLGA-based nanocarriers have attracted a lot of interest for IN delivery of drugs. This review gathered clear and concise statements of the recent progress of the various developed PLGA-based nanocarriers for IN drug delivery in brain diseases including Alzheimer’s, Parkinson’s, brain tumors, ischemia, epilepsy, depression, and schizophrenia. Subsequently, future perspectives and challenges of PLGA-based IN administration are discussed briefly

Association of social jetlag with gestational diabetes: Qazvin Maternal and Neonatal Metabolic Study

The association of social jetlag (SJL), as a quantitative measure of circadian misalignment, with insulin resistance and metabolic syndrome has been reported. The present study was designed to investigate the association of SJL with gestational diabetes mellitus (GDM). Pregnant women with gestational age ≤14 weeks were enrolled in this longitudinal study. The participants with pre-GDM, shift workers and those who used alarms for waking up on free days were excluded from the study. SJL as well as behavioral and psychological parameters were evaluated at enrollment. The participants were categorized based on each 1-h increment of SJL. The association of SJL with the occurrence of GDM in the late second trimester was evaluated using univariate and multivariate methods. In total, 821 pregnant women entered the study, and after omitting individuals with excluding criteria, analyses were performed on 557 participants. The frequencies of SJL < 1 h,1 ≤ SJL < 2 h and SJL ≥ 2 h were 44.7%, 37.2% and 18.1%, respectively. Average sleep duration was higher in SJL < 1 h compared with the two other groups (p < 0.001). During follow-up, 90 (16.1%) women with GDM were identified. SJL ≥ 2 h was associated with a 4.4-5.6 times higher risk of GDM in different models of adjustment (p < 0.05). Pregnant women with high SJL are at a higher risk of GDM. Further studies for evaluating the mechanisms by which SJL affects GDM are warranted

Oxford-MEST classification in IgA nephropathy patients: A report from Iran.

BACKGROUND There is a limited knowledge about the morphological features of IgA nephropathy (IgAN)in the middle east region. OBJECTIVES The objective of this study was to evaluate the spectrum of histopathological findings in IgAN patients at our laboratory. PATIENTS AND METHODS At this work, an observational study reported which was conducted on IgAN patients using the Oxford-MEST classification system. RESULTS In this survey, of 102 patients 71.6 % were male. The mean age of the patients was 37.7 ± 13.6 years. Morphologic variables of MEST classification was as follows; M1: 90.2 %, E: 32 %, S: 67 % also,T in grads I and II were in 30% and 19% respectively, while 51% were in grade zero. A significant difference was observed in segmental glomerulosclerosis (P=0.003) and interstitial fibrosis/tubular atrophy frequency distribution (P= 0.045), between males and females . Furthermore, it was found that mesangial hypercellularity was more prevalent in yonger patients. Moreover, there was a significant correlation between serum creatinine and crescents (P<0.001). There was also significant correlation of serum creatinine with segmental glomerulosclerosis (P<0.001). CONCLUSIONS Higher prevalence of segmental glomerulosclerosis and interstitial fibrosis/ tubular atrophy, as the two of, four variables of Oxford-MEST classification of IgAN in male patients further attests that male gender is a risk factor in this disease.In this study the significant correlation between serum creatinine and crescent was in an agreement with previous studies and suggests for the probable accomodation of extracapillary proliferation as a new variable in MEST system