Process Variation Aware DRAM (Dynamic Random Access Memory) Design Using Block-Based Adaptive Body Biasing Algorithm

Large dense structures like DRAMs (Dynamic Random Access Memory) are particularly susceptible to process variation, which can lead to variable latencies in different memory arrays. However, very little work exists on variation studies in DRAMs. This is due to the fact that DRAMs were traditionally placed off-chip and their latency changes due to process variation did not impact the overall processor performance. However, emerging technology trends like three-dimensional integration, use of sophisticated memory controllers, and continued scaling of technology node, substantially reduce DRAM access latency. Hence, future technology nodes will see widespread adoption of embedded DRAMs. This makes process variation a critical upcoming challenge in DRAMs that must be addressed in current and forthcoming technology generations. In this paper, techniques for modeling the effect of random, as well as spatial variation, in large DRAM array structures are presented. Sensitivity-based gate level process variation models combined with statistical timing analysis are used to estimate the impact of process variation on the DRAM performance and leakage power. A simulated annealing-based Vth assignment algorithm using adaptive body biasing is proposed in this thesis to improve the yield of DRAM structures. By applying the algorithm on a 1GB DRAM array, an average of 14.66% improvement in the DRAM yield is obtained

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 43)

Abstracts are provided for 128 patents and patent applications entered into the NASA scientific and technical information system during the period Jan. 1993 through Jun. 1993. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application

Technology Implications for Large Last-Level Caches

Large last-level cache (L3C) is efficient for bridging the performance and power gap between processor and memory. Several memory technologies, including SRAM, STT-RAM (MRAM), and embedded DRAM (eDRAM), have been used or considered as the technology to implement L3Cs. However, each of them has inherent weaknesses: SRAM is relatively low density and dissipates high leakage; STT-RAM has long write latency and requires high write energy; eDRAM requires refresh. As future processors are expected to have larger last-level caches, the objective of this dissertation is to study the tradeoffs associated with using each of these technologies to implement L3Cs. In order to make useful comparisons between L3Cs built with SRAM, STT-RAM, and eDRAM, we consider and implement several levels of details. First, to obtain unbiased cache performance and power properties (i.e., read/write access latency, read/write access energy, leakage power, refresh power, area), we prototype caches based on realistic memory and device models. Second, we present simplistic analytical models that enable us to quickly examine different memory technologies under various scenarios. Third, we review power-optimization techniques for each of the technologies, and propose using a low-cost dead-line prediction scheme for eDRAM-based L3Cs to eliminate unnecessary refreshes. Finally, the highlight of this dissertation is the comparison and analysis of low-leakage SRAM, low write-energy STT-RAM, and refresh-optimized eDRAM. We report system performance, last-level cache energy breakdown, and memory hierarchy energy breakdown, using an augmented full-system simulator with the execution of a range of workloads and input sets. From the insights gained through simulation results, STT-RAM has the highest potential to save energy in future L3C designs. For contemporary processors, SRAM-based L3C results in the fastest system performance, whereas eDRAM consumes the lowest energy

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 41)

Abstracts are provided for 131 patents and patent applications entered into the NASA scientific and technical information system during the period Jan. 1992 through Jun. 1992. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application

The Role of Information and Financial Reporting in Corporate Governance and Debt Contracting

We review recent literature on the role of financial reporting transparency in reducing governance-related agency conflicts among managers, directors, and shareholders, as well as in reducing agency conflicts between shareholders and creditors, and offer researchers some suggested avenues for future research. Key themes include the endogenous nature of debt contracts and governance mechanisms with respect to information asymmetry between contracting parties, the heterogeneous nature of the informational demands of contracting parties, and the heterogeneous nature of the resulting governance and debt contracts. We also emphasize the role of a commitment to financial reporting transparency in facilitating informal multiperiod contracts among managers, directors, shareholders, and creditors

A Prototype CVNS Distributed Neural Network

Artificial neural networks are widely used in many applications such as signal processing, classification, and control. However, the practical implementation of them is challenged by the number of inputs, storing the weights, and realizing the activation function.In this work, Continuous Valued Number System (CVNS) distributed neural networks are proposed which are providing the network with self-scaling property. This property aids the network to cope spontaneously with different number of inputs. The proposed CVNS DNN can change the dynamic range of the activation function spontaneously according to the number of inputs providing a proper functionality for the network.In addition, multi-valued CVNS DRAMs are proposed to store the weights as CVNS digits. These memories scan store up to 16 levels, equal to 4 bits, on each storage cell. In addition, they use error correction codes to detect and correct the error over the stored values.A synapse-neuron module is proposed to decrease the design cost. It contains both synapse and neuron and the relevant components. In these modules, the activation function is realized through analog circuits which are far more compact compared to the digital look-up-tables while quite accurate.Furthermore, the redundancy between CVNS digits together with the distributed structure of the neuron make the proposal stable against process violations and reduce the noise to signal ration

The Impact of Medical Spending Growth on Guaranteed Renewable Health insurance

I examine the problem of writing guaranteed renewable health insurance in the presence of medical spending growth. Prior research suggests that the growth and difficulty in forecasting future medical costs is an impediment to multiperiod health insurance, where contract reserves are used to pay a portion of the benefits in later years of the contract. Medical spending growth is an input to calculating the magnitude of premiums and reserves, so setting up reserves to pay future claims involves forecasting spending growth. Hedging assets can ameliorate the investment problem by providing assets that automatically adjust to unexpected shocks in spending growth. I expand an existing model of guaranteed renewability in an economy with risk to show the specific ways that medical spending growth enters the premium and reserve functions. I treat stochastic trend as a factor the insurance company can predict with error. I utilize aggregate and individual level insurance spending data and financial returns data to analyze whether medical trend can be hedged with existing assets. I separate trend into predictable and error components and analyze the correlation between the error component and return on assets. I find that medical spending growth is predictable with error over short and medium time horizons. I find that there is no significant correlation between asset returns and forecast errors across several broad asset classes. The combination of partially predictable spending growth and the absence of a hedging asset imply that insurers should be using reserves to manage the macroeconomic risk of spending growth. The load for reserving for trend is an up-front cost in addition to the up-front expense of guaranteed renewability. Insurers should use a diversified investment strategy for reserves rather than one targeted at trying to match spending growth. I conclude by noting the positive and negative effects of the newly passed health reform law (PPACA) on guaranteed renewable health insurance and other health insurance arrangements that require contract reserves and policies that shift health care spending onto public plans