N-SVRG:Stochastic Variance Reduction Gradient with Noise Reduction Ability for Small Batch Samples

The machine learning model converges slowly and has unstable training since large variance by random using a sample estimate gradient in SGD.To this end,we propose a noise reduction method for Stochastic Variance Reduction gradient(SVRG),called N-SVRG,which uses small batches samples instead of all samples for the average gradient calculation,while performing an incremental update of the average gradient.In each round of iteration,a small batch of samples is randomly selected for the average gradient calculation,while the average gradient is updated by rounding of the past model gradients during internal iterations.By suitably reducing the batch size B,the memory storage as well as the number of iterations can be reduced.The experiments are compared with the state-of-the-art Mini-Batch SGD,AdaGrad,RMSProp,SVRG and SCSG,and it is demonstrated that N-SVRG outperforms SVRG and SASG,and is on par with SCSG.Finally,by exploring the relationship between the small values of different parameters n.B and k and the effectiveness of the algorithm,we prove that ourN-SVRG algorithm has some stability and can achieve sufficient accuracy even in the case of small batch size.The advantages and disadvantages of various methods are experimentally compared,and the stability of N-SVRG is explored by parameter settings.

Polarization readout analysis for multilevel phase change recording by crystallization degree modulation

Four different states of Si15Sb85 and Ge2Sb2Te5 phase change memory thin films are obtained by crystallization degree modulation through laser initialization at different powers or annealing at different temperatures. The polarization characteristics of these two four-level phase change recording media are analyzed systematically. A simple and effective readout scheme is then proposed, and the readout signal is numerically simulated. The results show that a high-contrast polarization readout can be obtained in an extensive wavelength range for the four-level phase change recording media using common phase change materials. This study will help in-depth understanding of the physical mechanisms and provide technical approaches to multilevel phase change recording.

Writeback throttling in a virtualized system with SCM

Storage class memory （SCM） has the potential to revolutionize the memory landscape by its non-volatile and byte-addressable properties. However, there is little published work about exploring its usage for modem virtualized cloud infrastructure. We propose SCM-vWrite, a novel architecture designed around SCM, to ease the performance interference of virtualized storage subsystem. Through a case study on a typical virtualized cloud system, we first describe why cur- rent writeback manners are not suitable for a virtualized en- vironment, then design and implement SCM-vWrite to im- prove this problem. We also use typical benchmarks and re- alistic workloads to evaluate its performance. Compared with the traditional method on a conventional architecture, the ex- perimental result shows that SCM-vWrite can coordinate the writeback flows more effectively among multiple co-located guest operating systems, achieving a better disk I/O perfor- mance without any loss of reliability.