Matching Items (3)
Description
Modern-day integrated circuits are very capable, often containing more than a billion transistors. For example, the Intel Ivy Bridge 4C chip has about 1.2 billion transistors on a 160 mm2 die. Designing such complex circuits requires automation. Therefore, these designs are made with the help of computer aided design (CAD) tools. A major part of this custom design flow for application specific integrated circuits (ASIC) is the design of standard cell libraries. Standard cell libraries are a collection of primitives from which the automatic place and route (APR) tools can choose a collection of cells and implement the design that is being put together. To operate efficiently, the CAD tools require multiple views of each cell in the standard cell library. This data is obtained by characterizing the standard cell libraries and compiling the results in formats that the tools can easily understand and utilize.
My thesis focusses on the design and characterization of one such standard cell library in the ASAP7 7 nm predictive design kit (PDK). The complete design flow, starting from the choice of the cell architecture, design of the cell layouts and the various decisions made in that process to obtain optimum results, to the characterization of those cells using the Liberate tool provided by Cadence design systems Inc., is discussed in this thesis. The end results of the characterized library are used in the APR of a few open source register-transfer logic (RTL) projects and the efficiency of the library is demonstrated.
My thesis focusses on the design and characterization of one such standard cell library in the ASAP7 7 nm predictive design kit (PDK). The complete design flow, starting from the choice of the cell architecture, design of the cell layouts and the various decisions made in that process to obtain optimum results, to the characterization of those cells using the Liberate tool provided by Cadence design systems Inc., is discussed in this thesis. The end results of the characterized library are used in the APR of a few open source register-transfer logic (RTL) projects and the efficiency of the library is demonstrated.
ContributorsVangala, Manoj (Author) / Clark, Lawrence T (Thesis advisor) / Brunhaver, John S (Committee member) / Allee, David R. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Digital systems are increasingly pervading in the everyday lives of humans. The security of these systems is a concern due to the sensitive data stored in them. The physically unclonable function (PUF) implemented on hardware provides a way to protect these systems. Static random-access memories (SRAMs) are designed and used as a strong PUF to generate random numbers unique to the manufactured integrated circuit (IC).
Digital systems are important to the technological improvements in space exploration. Space exploration requires radiation hardened microprocessors which minimize the functional disruptions in the presence of radiation. The design highly efficient radiation-hardened microprocessor for enabling spacecraft (HERMES) is a radiation-hardened microprocessor with performance comparable to the commercially available designs. These designs are manufactured using a foundry complementary metal-oxide semiconductor (CMOS) 55-nm triple-well process. This thesis presents the post silicon validation results of the HERMES and the PUF mode of SRAM across process corners.
Chapter 1 gives an overview of the blocks implemented on the test chip 25. It also talks about the pre-silicon functional verification methodology used for the test chip. Chapter 2 discusses about the post silicon testing setup of test chip 25 and the validation of the setup. Chapter 3 describes the architecture and the test bench of the HERMES along with its testing results. Chapter 4 discusses the test bench and the perl scripts used to test the SRAM along with its testing results. Chapter 5 gives a summary of the post-silicon validation results of the HERMES and the PUF mode of SRAM.
Digital systems are important to the technological improvements in space exploration. Space exploration requires radiation hardened microprocessors which minimize the functional disruptions in the presence of radiation. The design highly efficient radiation-hardened microprocessor for enabling spacecraft (HERMES) is a radiation-hardened microprocessor with performance comparable to the commercially available designs. These designs are manufactured using a foundry complementary metal-oxide semiconductor (CMOS) 55-nm triple-well process. This thesis presents the post silicon validation results of the HERMES and the PUF mode of SRAM across process corners.
Chapter 1 gives an overview of the blocks implemented on the test chip 25. It also talks about the pre-silicon functional verification methodology used for the test chip. Chapter 2 discusses about the post silicon testing setup of test chip 25 and the validation of the setup. Chapter 3 describes the architecture and the test bench of the HERMES along with its testing results. Chapter 4 discusses the test bench and the perl scripts used to test the SRAM along with its testing results. Chapter 5 gives a summary of the post-silicon validation results of the HERMES and the PUF mode of SRAM.
ContributorsMedapuram, Sai Bharadwaj (Author) / Clark, Lawrence T (Thesis advisor) / Allee, David R. (Committee member) / Brunhaver, John S (Committee member) / Arizona State University (Publisher)
Created2017
Description
As device and voltage scaling cease, ever-increasing performance targets can only be achieved through the design of parallel, heterogeneous architectures. The workloads targeted by these domain-specific architectures must be designed to leverage the strengths of the platform: a task that has proven to be extremely difficult and expensive.
Machine learning has the potential to automate this process by understanding the features of computation that optimize device utilization and throughput.
Unfortunately, applications of this technique have utilized small data-sets and specific feature extraction, limiting the impact of their contributions.
To address this problem I present Dash-Database; a repository of C and C++ programs for software-defined radio applications and its neighboring fields; a methodology for structuring the features of computation using kernels, and a set of evaluation metrics to standardize computation data sets. Dash-Database contributes a general data set that supports machine understanding of computation and standardizes the input corpus utilized for machine learning of computation; currently only a small set of benchmarks and features are being used.
I present an evaluation of Dash-Database using three novel metrics: breadth, depth and richness; and compare its results to a data set largely representative of those used in prior work, indicating a 5x increase in breadth, 40x increase in depth, and a rich set of sample features.
Using Dash-Database, the broader community can work toward a general machine understanding of computation that can automate the design of workloads for domain-specific computation.
Machine learning has the potential to automate this process by understanding the features of computation that optimize device utilization and throughput.
Unfortunately, applications of this technique have utilized small data-sets and specific feature extraction, limiting the impact of their contributions.
To address this problem I present Dash-Database; a repository of C and C++ programs for software-defined radio applications and its neighboring fields; a methodology for structuring the features of computation using kernels, and a set of evaluation metrics to standardize computation data sets. Dash-Database contributes a general data set that supports machine understanding of computation and standardizes the input corpus utilized for machine learning of computation; currently only a small set of benchmarks and features are being used.
I present an evaluation of Dash-Database using three novel metrics: breadth, depth and richness; and compare its results to a data set largely representative of those used in prior work, indicating a 5x increase in breadth, 40x increase in depth, and a rich set of sample features.
Using Dash-Database, the broader community can work toward a general machine understanding of computation that can automate the design of workloads for domain-specific computation.
ContributorsWillis, Benjamin Roy (Author) / Brunhaver, John S (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2020