Resource and thermal management in 3D-stacked multi-/many-core systems

Continuous semiconductor technology scaling and the rapid increase in computational needs have stimulated the emergence of multi-/many-core processors. While up to hundreds of cores can be placed on a single chip, the performance capacity of the cores cannot be fully exploited due to high latencies of interconnects and memory, high power consumption, and low manufacturing yield in traditional (2D) chips. 3D stacking is an emerging technology that aims to overcome these limitations of 2D designs by stacking processor dies over each other and using through-silicon-vias (TSVs) for on-chip communication, and thus, provides a large amount of on-chip resources and shortens communication latency. These benefits, however, are limited by challenges in high power densities and temperatures. 3D stacking also enables integrating heterogeneous technologies into a single chip. One example of heterogeneous integration is building many-core systems with silicon-photonic network-on-chip (PNoC), which reduces on-chip communication latency significantly and provides higher bandwidth compared to electrical links. However, silicon-photonic links are vulnerable to on-chip thermal and process variations. These variations can be countered by actively tuning the temperatures of optical devices through micro-heaters, but at the cost of substantial power overhead. This thesis claims that unearthing the energy efficiency potential of 3D-stacked systems requires intelligent and application-aware resource management. Specifically, the thesis improves energy efficiency of 3D-stacked systems via three major components of computing systems: cache, memory, and on-chip communication. We analyze characteristics of workloads in computation, memory usage, and communication, and present techniques that leverage these characteristics for energy-efficient computing. This thesis introduces 3D cache resource pooling, a cache design that allows for flexible heterogeneity in cache configuration across a 3D-stacked system and improves cache utilization and system energy efficiency. We also demonstrate the impact of resource pooling on a real prototype 3D system with scratchpad memory. At the main memory level, we claim that utilizing heterogeneous memory modules and memory object level management significantly helps with energy efficiency. This thesis proposes a memory management scheme at a finer granularity: memory object level, and a page allocation policy to leverage the heterogeneity of available memory modules and cater to the diverse memory requirements of workloads. On the on-chip communication side, we introduce an approach to limit the power overhead of PNoC in (3D) many-core systems through cross-layer thermal management. Our proposed thermally-aware workload allocation policies coupled with an adaptive thermal tuning policy minimize the required thermal tuning power for PNoC, and in this way, help broader integration of PNoC. The thesis also introduces techniques in placement and floorplanning of optical devices to reduce optical loss and, thus, laser source power consumption.2018-03-09T00:00:00

Modeling and Solving Resource Constrained Project Scheduling Problems with Remanufacturing Activities

Resource constrained project scheduling problem (RCPSP) is one of the most important problems in industrial engineering and production management. Owing to environmental concerns, companies are paying more attention to the remanufacturing of end-of-life products. In this thesis, a mathematical model is developed considering remanufacturing activities in resource constrained project scheduling problem. The mathematical model considers recycle rate in multiple operation modes and several components of cost, including bonus, penalty, and others. A set of project network instance are generated using RanGen1 for evaluation. To solve the model, a three-stage heuristic method is developed in CPLEX 12.8 environment. Result shows that proposed method can reach a close-to-optimal solution within acceptable time limit

State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm

A new approach to generate the original motion data for humanoid motion planning is presented in this paper. And a state generator is developed based on the genetic algorithm, which enables users to generate various motion states without using any reference motion data. By specifying various types of constraints such as configuration constraints and contact constraints, the state generator can generate stable states that satisfy the constraint conditions for humanoid robots. To deal with the multiple constraints and inverse kinematics, the state generation is finally simplified as a problem of optimizing and searching. In our method, we introduce a convenient mathematic representation for the constraints involved in the state generator, and solve the optimization problem with the genetic algorithm to acquire a desired state. To demonstrate the effectiveness and advantage of the method, a number of motion states are generated according to the requirements of the motion

Model Predictive Control for a Small Scale Unmanned Helicopter

Kinematical and dynamical equations of a small scale unmanned helicoper are presented in the paper. Based on these equations a model predictive control (MPC) method is proposed for controlling the helicopter. This novel method allows the direct accounting for the existing time delays which are used to model the dynamics of actuators and aerodynamics of the main rotor. Also the limits of the actuators are taken into the considerations during the controller design. The proposed control algorithm was verified in real flight experiments where good perfomance was shown in postion control mode

Sodium-glucose co-transporter-2 inhibitors and risk of adverse renal outcomes among patients with type 2 diabetes: A network and cumulative meta-analysis of randomized controlled trials

Aim To compare the associations of individual sodium-glucose co-transporter-2 (SGLT2) inhibitors with adverse renal outcomes in patients with type 2 diabetes mellitus (T2DM). Methods PubMed, EMBASE, CENTRAL and ClinicalTrials.gov were searched for studies published up to May 24, 2016, without language or date restrictions. Randomized trials that reported at least 1 renal-related adverse outcome in patients with T2DM treated with SGLT2 inhibitors were included. Pairwise and network meta-analyses were carried out to calculate the odds ratios (ORs) with 95% confidence intervals (CIs), and a cumulative meta-analysis was performed to assess the robustness of evidence. Results In total, we extracted 1334 composite renal events among 39 741 patients from 58 trials, and 511 acute renal impairment/failure events among 36 716 patients from 53 trials. Dapagliflozin was significantly associated with a greater risk of composite renal events than placebo (OR 1.64, 95% CI 1.26-2.13). Empagliflozin seemed to confer a lower risk than placebo (OR 0.63, 95% CI 0.54-0.72), canagliflozin (OR 0.48, 95% CI 0.29-0.82) and dapagliflozin (OR 0.38, 95% CI 0.28-0.51). With regard to acute renal impairment/failure, only empagliflozin was significantly associated with a lower risk than placebo (OR 0.72, 95% CI 0.60-0.86). The cumulative meta-analysis indicated the robustness of our significant findings. Conclusions The present meta-analysis indicated that dapagliflozin may increase the risk of adverse renal events, while empagliflozin may have a protective effect among patients with T2DM. Further data from large well-conducted randomized controlled trials and a real-world setting are warranted

Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)

Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts recommender quality especially for the item slices without much user feedback. While there have been many research advances made in academia, deploying these methods in production is very difficult and very few improvements have been made in industry. One challenge is that these methods often hurt overall performance; additionally, they could be complex and expensive to train and serve. In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost. We first find that the predictions of user preferences are biased under long-tail distributions. The bias comes from the differences between training and serving data in two perspectives: 1) the item distributions, and 2) user's preference given an item. Most existing methods mainly attempt to reduce the bias from the item distribution perspective, ignoring the discrepancy from user preference given an item. This leads to a severe forgetting issue and results in sub-optimal performance. To address the problem, we design a novel Cross Decoupling Network (CDN) (i) decouples the learning process of memorization and generalization on the item side through a mixture-of-expert architecture; (ii) decouples the user samples from different distributions through a regularized bilateral branch network. Finally, a new adapter is introduced to aggregate the decoupled vectors, and softly shift the training attention to tail items. Extensive experimental results show that CDN significantly outperforms state-of-the-art approaches on benchmark datasets. We also demonstrate its effectiveness by a case study of CDN in a large-scale recommendation system at Google.Comment: Accepted by KDD 2023 Applied Data Science (ADS) trac

PlantQTL-GE: a database system for identifying candidate genes in rice and Arabidopsis by gene expression and QTL information

We have designed and implemented a web-based database system, called PlantQTL-GE, to facilitate quantitatine traits locus (QTL) based candidate gene identification and gene function analysis. We collected a large number of genes, gene expression information in microarray data and expressed sequence tags (ESTs) and genetic markers from multiple sources of Oryza sativa and Arabidopsis thaliana. The system integrates these diverse data sources and has a uniform web interface for easy access. It supports QTL queries specifying QTL marker intervals or genomic loci, and displays, on rice or Arabidopsis genome, known genes, microarray data, ESTs and candidate genes and similar putative genes in the other plant. Candidate genes in QTL intervals are further annotated based on matching ESTs, microarray gene expression data and cis-elements in regulatory sequences. The system is freely available at