Constructing Reliable Super Dense Phase Change Memory under Write Disturbance

Phase Change Memory (PCM) has better scalability and smaller cell size comparing to DRAM. However, further scaling PCM cell in deep sub-micron regime results in significant thermal based write disturbance. Naively allocating large inter-cell space increases cell size from ideal 4F^2 to 12F^2. While a recent work mitigates write disturbance along word-lines through disturbance resilient data encoding, which can shrink PCM cell size from 12F^2 to 8F^2, it is ineffective for write disturbance along bit-lines, which is more severe due to widely adopted uTrench structure in constructing PCM cell arrays. In this thesis, we propose SD-PCM, an architecture to achieve reliable write operations in Super Dense PCM. In particular, we focus on mitigating write disturbance along bit-lines such that we can construct super dense PCM chips with 4F^2 cell size, i.e., the minimal for diode-switch based PCM. Based on simple verification-n-correction (VnC), we propose LazyCorrection and PreRead to effectively reduce VnC overhead and minimize cascading verification during write. We further propose (n:m)-Alloc for achieving good tradeoff between VnC overhead minimization and memory capacity loss. Our experimental results show that, comparing to a write disturbance-free low density PCM, SD-PCM achieves 80% capacity improvement in cell arrays while incurring around 0-10% performance degradation when using different (n:m) allocators

The role of (shared) genetics and environment in (co-occurring) psychiatric problems, substance use, and obesity

Psychiatric disorders, substance use, and obesity often co-occur. These problems not only co-occur within the same person but also co-aggregate within families. However, the mechanisms underlying their co-occurrence are not fully understood. This thesis aims to investigate the influence of (shared) genetic and environmental factors on psychiatric problems, substance use, obesity, and their co-occurrence. We used data from the large multi-generational Lifelines Cohort Study to estimate familial aggregation, co-aggregation, heritability, and genetic correlations, and explored interactions of genetic risk with environmental factors (i.e., stress exposures and socioeconomic status [SES]). Additionally, we combined family history and polygenic risk scores to predict diseases using between-family and within-family approaches. The findings in this thesis offer insights into familial co-aggregation and shared genetics between psychiatric problems, obesity, and substance use. Moreover, the thesis provides evidence for the presence of interactions between polygenic risk and stress exposures in relation to depression and anxiety, as well as interactions between polygenic risk and SES in relation to depression, anxiety, BMI, smoking, and alcohol use. In the latter study, interaction effects were smaller at the aggregated genetic and outcome levels, suggesting that modelling aggregated genetic predictors and aggregated disease outcomes did not improve etiological understanding. Finally, the thesis demonstrates that combining family history and polygenic risk scores (PRSs) lead to improved prediction for obesity outcomes. The comparison of within-family and between-family effects of polygenic risk, revealed that little bias caused by, such as gene-environment correlations, was found in the genetic estimates for indices of obesity

Research on quantitative mechanism of Kirchhoff diffraction acoustical holography

In the method of far-field acoustic holographic noise identification, the Kirchhoff formula has been widely used. Achievement of reconstruction is the outstanding advantage of acoustical holography over the widely-used beam-forming method. In this paper, the mechanism of acoustical holography based on finite Kirchhoff diffraction is established, the relationship between holographic aperture angle and the quantitative accuracy is revealed. With simulations for known sound source, the quantitative mechanism is validated successfully

DESTINY: A Comprehensive Tool with 3D and Multi-Level Cell Memory Modeling Capability

To enable the design of large capacity memory structures, novel memory technologies such as non-volatile memory (NVM) and novel fabrication approaches, e.g., 3D stacking and multi-level cell (MLC) design have been explored. The existing modeling tools, however, cover only a few memory technologies, technology nodes and fabrication approaches. We present DESTINY, a tool for modeling 2D/3D memories designed using SRAM, resistive RAM (ReRAM), spin transfer torque RAM (STT-RAM), phase change RAM (PCM) and embedded DRAM (eDRAM) and 2D memories designed using spin orbit torque RAM (SOT-RAM), domain wall memory (DWM) and Flash memory. In addition to single-level cell (SLC) designs for all of these memories, DESTINY also supports modeling MLC designs for NVMs. We have extensively validated DESTINY against commercial and research prototypes of these memories. DESTINY is very useful for performing design-space exploration across several dimensions, such as optimizing for a target (e.g., latency, area or energy-delay product) for a given memory technology, choosing the suitable memory technology or fabrication method (i.e., 2D v/s 3D) for a given optimization target, etc. We believe that DESTINY will boost studies of next-generation memory architectures used in systems ranging from mobile devices to extreme-scale supercomputers. The latest source-code of DESTINY is available from the following git repository: https://bitbucket.org/sparsh_mittal/destiny_v2

Towards Efficient Secure Memory Systems with Oblivious RAM

When multiple users and applications share the resources on cloud servers, information may be leaked through hidden channels related to the memory. Encryption can help to protect data privacy. However, the physical address on the memory bus cannot be encrypted if there is no computation power on memory DIMM. The attacker may observe clear-text physical address access frequency and infer sensitive information in the program. To completely protect the system from address access pattern leakage, we need to use Oblivious RAM, which obfuscates the physical address by remapping it after each access. However, the ORAM access is still costly regarding bandwidth. In this dissertation, I focus on discussing and designing efficient and scalable secure memory systems with ORAM. Firstly, I studied the co-run interference between different applications on the modern computer servers. We found out that how to allocate shared resources between secure applications and other normal applications will determine the overall system performance. I proposed Cooperative-ORAM protocol, which achieves the goal of better resource allocation, utilization and same security guarantee as original ORAM design. Our design delivers an average of 20% overall performance improvement over the baseline Path ORAM design while providing a flexible resource tuning between different kinds of applications. In the next part, I address the problems when the application number further scales on the same server. The co-run interference and memory traffic will be more intense when we scale the number of applications on the server. Meanwhile, more applications mean that the demand for memory capacity is also increasing. I proposed the design of D-ORAM, which delegate the ORAM based secure engine on Buffer-on-Board(BoB), which is in between of the last level cache and main memory, to enable high-level privacy protection and low execution interference on cloud servers. By pushing the ORAM engine off-chip, most of the ORAM accesses will not need to be sent back to the processor side, which removes the excessive data movement overhead. Our evaluation shows that D-ORAM improves normal applications performance by 22.5% on average