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Articles 1 - 5 of 5
Full-Text Articles in Computer Engineering
Arithmetic Logic Unit Architectures With Dynamically Defined Precision, Getao Liang
Arithmetic Logic Unit Architectures With Dynamically Defined Precision, Getao Liang
Doctoral Dissertations
Modern central processing units (CPUs) employ arithmetic logic units (ALUs) that support statically defined precisions, often adhering to industry standards. Although CPU manufacturers highly optimize their ALUs, industry standard precisions embody accuracy and performance compromises for general purpose deployment. Hence, optimizing ALU precision holds great potential for improving speed and energy efficiency. Previous research on multiple precision ALUs focused on predefined, static precisions. Little previous work addressed ALU architectures with customized, dynamically defined precision. This dissertation presents approaches for developing dynamic precision ALU architectures for both fixed-point and floating-point to enable better performance, energy efficiency, and numeric accuracy. These new …
Skybridge: A New Nanoscale 3-D Computing Framework For Future Integrated Circuits, Mostafizur Rahman
Skybridge: A New Nanoscale 3-D Computing Framework For Future Integrated Circuits, Mostafizur Rahman
Doctoral Dissertations
Continuous scaling of CMOS has been the major catalyst in miniaturization of integrated circuits (ICs) and crucial for global socio-economic progress. However, continuing the traditional way of scaling to sub-20nm technologies is proving to be very difficult as MOSFETs are reaching their fundamental performance limits [1] and interconnection bottleneck is dominating IC operational power and performance [2]. Migrating to 3-D, as a way to advance scaling, has been elusive due to inherent customization and manufacturing requirements in CMOS architecture that are incompatible with 3-D organization. Partial attempts with die-die [3] and layer-layer [4] stacking have their own limitations [5]. We …
On Thermal Sensor Calibration And Software Techniques For Many-Core Thermal Management, Shiting Lu
On Thermal Sensor Calibration And Software Techniques For Many-Core Thermal Management, Shiting Lu
Doctoral Dissertations
The high power density of a many-core processor results in increased temperature which negatively impacts system reliability and performance. Dynamic thermal management applies thermal-aware techniques at run time to avoid overheating using temperature information collected from on-chip thermal sensors. Temperature sensing and thermal control schemes are two critical technologies for successfully maintaining thermal safety. In this dissertation, on-line thermal sensor calibration schemes are developed to provide accurate temperature information. Software-based dynamic thermal management techniques are proposed using calibrated thermal sensors. Due to process variation and silicon aging, on-chip thermal sensors require periodic calibration before use in DTM. However, the calibration …
Design And Implementation Of An Economy Plane For The Internet, Xinming Chen
Design And Implementation Of An Economy Plane For The Internet, Xinming Chen
Doctoral Dissertations
The Internet has been very successful in supporting many network applications. As the diversity of uses for the Internet has increased, many protocols and services have been developed by the industry and the research community. However, many of them failed to get deployed in the Internet. One challenge of deploying these novel ideas in operational network is that the network providers need to be involved in the process. Many novel network protocols and services, like multicast and end-to-end QoS, need the support from network providers. However, since network providers are typically driven by business reasons, if they can not get …
Physically Equivalent Intelligent Systems For Reasoning Under Uncertainty At Nanoscale, Santosh Khasanvis
Physically Equivalent Intelligent Systems For Reasoning Under Uncertainty At Nanoscale, Santosh Khasanvis
Doctoral Dissertations
Machines today lack the inherent ability to reason and make decisions, or operate in the presence of uncertainty. Machine-learning methods such as Bayesian Networks (BNs) are widely acknowledged for their ability to uncover relationships and generate causal models for complex interactions. However, their massive computational requirement, when implemented on conventional computers, hinders their usefulness in many critical problem areas e.g., genetic basis of diseases, macro finance, text classification, environment monitoring, etc. We propose a new non-von Neumann technology framework purposefully architected across all layers for solving these problems efficiently through physical equivalence, enabled by emerging nanotechnology. The architecture builds …