Indiana University-Purdue University Indianapolis (IUPUI) / A novel method of power management and sequential monitoring of several sensors is proposed in this work. Application specific integrated circuits (ASICs) consisting of analog and digital sub-systems forming a system on chip (SoC) has been designed using complementary metal-oxide-semiconductor (CMOS) technology. The analog sub-system comprises the sensor-drivers that convert the input voltage variations to output pulse-frequency. The digital sub-system includes the system management unit (SMU), counter, and shift register modules. This performs the power-usagemanagement, sensor-sequence-control, and output-data-frame-generation functions. The SMU is the key unit within the digital sub-system is that enables or disables a sensor. It captures the pulse waves from a sensor for 3 clocks out of a 16-clock cycle, and transmits the signal to the counter modules. As a result, the analog sub-system is at on-state for only 3/16th fraction (18 %) of the time, leading to reduced power consumption. Three cycles is an optimal number selected for the presented design as the system is unstable with less than 3 cycles and higher clock cycles results in increased power consumption. However, the system can achieve both higher sensitivity and better stability with increased on-state clock cycles. A current-starved-ring-oscillator generates pulse waves that depend on the sensor input parameter. By counting the number of pulses of a sensor-driver in one clock cycle, a sensor input parameter is converted to digital. The digital sub-system constructs a 16-bit frame consisting of 8-bit sensor data, start and stop bits, and a parity bit. Ring oscillators that drive capacitance and resistance-based sensors use an arrangement of delay elements with two levels of control voltages. A bias unit which provides these two levels of control voltages consists of CMOS cascade current mirror to maximize voltage swing for control voltage level swings which give the oscillator wider tuning range and lower temperature induced variations. The ring oscillator was simulated separately for 250 nm and 180 nm CMOS technologies. The simulation results show that when the input voltage of the oscillator is changed by 1 V, the output frequency changes linearly by 440 MHz for 180 nm technology and 206 MHz for 250 nm technology. In a separate design, a temperature sensitive ring oscillator with symmetrical load and temperature dependent input voltage was implemented. When the temperature in the simulation model was varied from -50C to 100C the oscillator output frequency reduced by 510 MHz for the 250 nm and by 810 MHz for 180 nm CMOS technologies, respectively. The presented system does not include memory unit, thus, the captured sensor data has to be instantaneously transmitted to a remote station, e.g. end user interface. This may result in a loss of sensor data in an event of loss of communication link with the remote station. In addition, the presented design does not include transmitter and receiver modules, and thus necessitates the use of separate modules for the transfer of the data.

Thesis (D. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, 2012 / This thesis describes a method for the automatic generation of appli- cation speci_c processors. The thesis was organized into three sepa- rate but interrelated studies, which together provide: a justi_cation for the method used, a theory that supports the method, and a soft- ware application that realizes the method. The _rst study looked at how modern day microprocessors utilize their hardware resources and it proposed a metric, called core density, for measuring the utilization rate. The core density is a function of the microprocessor's instruction set and the application scheduled to run on that microprocessor. This study concluded that modern day microprocessors use their resources very ine_ciently and proposed the use of subset processors to exe- cute the same applications more e_ciently. The second study sought to provide a theoretical framework for the use of subset processors by developing a generic formal model of computer architecture. To demonstrate the model's versatility, it was used to describe a number of computer architecture components and entire computing systems. The third study describes the development of a set of software tools that enable the automatic generation of application speci_c proces- sors. The FiT toolkit automatically generates a unique Hardware Description Language (HDL) description of a processor based on an application binary _le and a parameterizable template of a generic mi- croprocessor. Area-optimized and performance-optimized custom soft processors were generated using the FiT toolkit and the utilization of the hardware resources by the custom soft processors was character- ized. The FiT toolkit was combined with an ANSI C compiler and a third-party tool for programming _eld-programmable gate arrays (FPGAs) to create an unconstrained C-to-silicon compiler.

No description available.

Recently, microelectronics designs have reached extremely high operating frequencies as well as very small die and package sizes. This has made signal integrity an important bottleneck in the design process, and resulted in the inclusion of signal integrity simulation in the computer aided design flow. However, such simulations are often difficult because in many cases it is impossible to derive analytical models for certain passive elements, and the only available data are frequency-domain measurements or full-wave simulations. Furthermore, at such high frequencies these components are distributed in nature and require a large number of poles to be properly characterized. Simple lumped equivalent circuits are therefore difficult to obtain, and more systematic approaches are required. In this thesis we study the Vector Fitting techniques for obtaining such equivalent model and propose a more streamlined approach for preserving passivity while maintaining accuracy.

Video applications are becoming ubiquitous in recent times due to an explosion in the number of devices with video capture and display capabilities. Traditionally, video applications are implemented on a variety of devices with each device targeting a specific application. However, the advances in technology have created a need to support multiple applications from a single device like a smart phone or tablet. Such convergence of applications necessitates support for interoperability among various applications, scalable performance meet the requirements of different applications and a high degree of reconfigurability to accommodate rapid evolution in applications features. In addition, low power consumption requirement is also very stringent for many video applications. The conventional custom hardware implementations of video applications deliver high performance at low power consumption while the recent MPSoC implementations enable high degree of interoperability and are useful to support application evolution. In this thesis, we combine the best features of custom hardware and MPSoC approaches to design a Polymorphic ASIC. A Polymorphic ASIC is an integrated circuit designed to meet the requirements of several applications belonging to a particular domain. A polymorphic ASIC consists of a fabric of computation, storage and communication resources, using which applications are composed dynamically. Although different video applications differ widely in the internal de-tails of operation, at the heart of almost every video application is a video codec (encoder and decoder). The requirements of scalability, high performance and low power consumption are very stringent for video decoding. Therefore this thesis focuses mainly on the architectural design of a Polymorphic ASIC for video decoding. We present an unified software and hardware architecture (USHA) for Polymorphic ASIC. USHA is a tiled architecture which uses loosely coupled processor and hardware tiles that are software programmable and hardware reconfigurable respectively. The distinctive feature of Polymorphic ASIC is the static partitioning of the application and dynamic mapping of ap-plication processes onto the computational tiles. Depending on the application scenarios, a process may be mapped onto one of the hardware or processor tiles. Polymorphic ASIC incor-porates a network–on–chip (NoC) to achieve flexible communication across different tiles. Formulation of a programming framework for Polymorphic ASIC requires an implementation model that captures the structure of video decoder applications as well as the properties of the Polymorphic ASIC architecture. We derive an implementation model based on a combination of parametric polyhedral process networks, stream based functions and windowed dataflow models of computation. The implementation model leads to a process network oriented compilation flow that achieves realization agnostic application partitioning and enables seamless migration across uniprocessor, multi–processor, semi hardware and full hardware configurations of a video decoder. The thesis also presents an application QoS aware scheduler that selects a decoder configuration that best meets the application performance requirements, thereby enabling dynamic performance scaling. The memory hierarchy of Polymorphic ASIC makes use of an application specific cache. Through a combined analysis of miss rate and external memory bandwidth, we show that the degradation in decoder performance due to memory stall cycles depends on the properties of the video being decoded as well as the behavior of the external memory interface. Based on this observation, we present the design of a reconfigurable 2–D cache architecture which can adjust its parameters in accordance with the characteristics of the video stream being decoded. We validate the Polymorphic ASIC using a proof–of–concept implementation on an FPGA. The performance of H.264 decoder on Polymorphic ASIC is evaluated for uniprocessor, multi processor, hardware accelerated and full hardware configurations. The scaling in performance delivered by these configurations shows that the Polymorphic ASIC enables the application to achieve super linear speedups [1]. The experimental results show that different implementations of a H.264 video decoder on the Polymorphic ASIC can deliver performance comparable to a wide spectrum of devices ranging from embedded processor like ARM 9 to MPSoCs like IBM Cell. We also present the energy consumption of various configurations of video decoders on Polymorphic ASIC and an application to configuration mapping aimed at minimizing the overall energy consumption of a Polymorphic ASIC.

Video applications are becoming ubiquitous in recent times due to an explosion in the number of devices with video capture and display capabilities. Traditionally, video applications are implemented on a variety of devices with each device targeting a specific application. However, the advances in technology have created a need to support multiple applications from a single device like a smart phone or tablet. Such convergence of applications necessitates support for interoperability among various applications, scalable performance meet the requirements of different applications and a high degree of reconfigurability to accommodate rapid evolution in applications features. In addition, low power consumption requirement is also very stringent for many video applications. The conventional custom hardware implementations of video applications deliver high performance at low power consumption while the recent MPSoC implementations enable high degree of interoperability and are useful to support application evolution. In this thesis, we combine the best features of custom hardware and MPSoC approaches to design a Polymorphic ASIC. A Polymorphic ASIC is an integrated circuit designed to meet the requirements of several applications belonging to a particular domain. A polymorphic ASIC consists of a fabric of computation, storage and communication resources, using which applications are composed dynamically. Although different video applications differ widely in the internal de-tails of operation, at the heart of almost every video application is a video codec (encoder and decoder). The requirements of scalability, high performance and low power consumption are very stringent for video decoding. Therefore this thesis focuses mainly on the architectural design of a Polymorphic ASIC for video decoding. We present an unified software and hardware architecture (USHA) for Polymorphic ASIC. USHA is a tiled architecture which uses loosely coupled processor and hardware tiles that are software programmable and hardware reconfigurable respectively. The distinctive feature of Polymorphic ASIC is the static partitioning of the application and dynamic mapping of ap-plication processes onto the computational tiles. Depending on the application scenarios, a process may be mapped onto one of the hardware or processor tiles. Polymorphic ASIC incor-porates a network–on–chip (NoC) to achieve flexible communication across different tiles. Formulation of a programming framework for Polymorphic ASIC requires an implementation model that captures the structure of video decoder applications as well as the properties of the Polymorphic ASIC architecture. We derive an implementation model based on a combination of parametric polyhedral process networks, stream based functions and windowed dataflow models of computation. The implementation model leads to a process network oriented compilation flow that achieves realization agnostic application partitioning and enables seamless migration across uniprocessor, multi–processor, semi hardware and full hardware configurations of a video decoder. The thesis also presents an application QoS aware scheduler that selects a decoder configuration that best meets the application performance requirements, thereby enabling dynamic performance scaling. The memory hierarchy of Polymorphic ASIC makes use of an application specific cache. Through a combined analysis of miss rate and external memory bandwidth, we show that the degradation in decoder performance due to memory stall cycles depends on the properties of the video being decoded as well as the behavior of the external memory interface. Based on this observation, we present the design of a reconfigurable 2–D cache architecture which can adjust its parameters in accordance with the characteristics of the video stream being decoded. We validate the Polymorphic ASIC using a proof–of–concept implementation on an FPGA. The performance of H.264 decoder on Polymorphic ASIC is evaluated for uniprocessor, multi processor, hardware accelerated and full hardware configurations. The scaling in performance delivered by these configurations shows that the Polymorphic ASIC enables the application to achieve super linear speedups [1]. The experimental results show that different implementations of a H.264 video decoder on the Polymorphic ASIC can deliver performance comparable to a wide spectrum of devices ranging from embedded processor like ARM 9 to MPSoCs like IBM Cell. We also present the energy consumption of various configurations of video decoders on Polymorphic ASIC and an application to configuration mapping aimed at minimizing the overall energy consumption of a Polymorphic ASIC.

No description available.

Current electrical systems are faced with the limitation in performance by the electrical interconnect technology determining overall processing speed. In addition, the electrical interconnects containing many long distance interconnects require high power to drive. One of the best ways to overcome these bottlenecks is through the use of optical interconnect to limit interconnect latency and power. This research explores new computer-aided design algorithms for developing optoelectronic systems. These algorithms focus on place and route problems using optical interconnections covering system-on-a-chip design as well as system-on-a-package design. In order to design optoelectronic systems, optical interconnection models are developed at first. The CAD algorithms include optical interconnection models and solve place and route problems for optoelectronic systems. The MCNC and GSRC benchmark circuits are used to evaluate these algorithms.

This thesis aims to create effective and comprehensive learning materials for students enrolled in the Switching Theory and Digital Design course. The lab is designed to enable students to program an FPGA using VHDL in the Quartus programming environment to control traffic intersections with sensors and traffic signals. This laboratory aims to provide students with practical experience in digital engineering design and help them develop the necessary skills to program and implement state machines for regulating traffic environments

Accurate estimation of voltage drop (IR drop), in Application-Specific Integrated Circuits (ASICs) is a critical challenge, which impacts their performance and power consumption. As technology advances and die sizes shrink, predicting IR drop fast and accurate becomes increasingly challenging. This thesis focuses on exploring the application of Machine Learning (ML) algorithms, including Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN) and Graph Neural Network (GNN), to address this problem. Traditional methods of estimating IR drop using commercial tools are time consuming, especially for complex designs with millions of transistors. To overcome that, ML algorithms are investigated for their ability to provide fast and accurate IR drop estimation. This thesis utilizes electrical, timing and physical features of the ASIC design as input to train the ML models. The scalability of the selected features allows for their effective application across various ASIC designs with very few adjustments. Experimental results demonstrate the advantages of ML models over commercial tools, offering significant improvements in prediction speed. Notably, GNNs, such as Graph Convolutional Network (GCN) models showed promising performance with low prediction errors in voltage drop estimation. The incorporation of graph-structures models opens new fields of research for accurate IR drop prediction. The conclusions drawn emphasize the effectiveness of ML algorithms in accurately estimating IR drop, thereby optimizing ASIC design efficiency. The application of ML models enables faster predictions and noticeably reducing calculation time. This contributes to enhancing energy efficiency and minimizing environmental impact through optimised power circuits. Future work can focus on exploring the scalability of the models by training on a smaller portion of the circuit and extrapolating predictions to the entire design seems promising for more efficient and accurate IR drop estimation in complex ASIC designs. These advantages present new opportunities in the field and extend the capabilities of ML algorithms in the task of IR drop prediction. / Noggrann uppskattning av spänningsfallet (IR-fall), i ASIC är en kritisk utmaning som påverkar deras prestanda och strömförbrukning. När tekniken går framåt och formstorlekarna krymper, blir det allt svårare att förutsäga IR-fall snabbt och exakt. Denna avhandling fokuserar på att utforska tillämpningen av ML-algoritmer, inklusive XGBoost, CNN och GNN, för att lösa detta problem. Traditionella metoder för att uppskatta IR-fall med kommersiella verktyg är tidskrävande, särskilt för komplexa konstruktioner med miljontals transistorer. För att övervinna det undersöks ML-algoritmer för deras förmåga att ge snabb och exakt IR-falluppskattning. Denna avhandling använder elektriska, timing och fysiska egenskaper hos ASIC-designen som input för att träna ML-modellerna. Skalbarheten hos de valda funktionerna möjliggör deras effektiva tillämpning över olika ASIC-designer med mycket få justeringar. Experimentella resultat visar fördelarna med ML-modeller jämfört med kommersiella verktyg, och erbjuder betydande förbättringar i förutsägelsehastighet. Noterbart är att GNNs, såsom GCN-modeller, visade lovande prestanda med låga prediktionsfel vid uppskattning av spänningsfall. Införandet av grafstrukturmodeller öppnar nya forskningsfält för exakt IRfallförutsägelse. De slutsatser som dras betonar effektiviteten hos MLalgoritmer för att noggrant uppskatta IR-fall, och därigenom optimera ASICdesigneffektiviteten. Tillämpningen av ML-modeller möjliggör snabbare förutsägelser och märkbart minskad beräkningstid. Detta bidrar till att förbättra energieffektiviteten och minimera miljöpåverkan genom optimerade kraftkretsar. Framtida arbete kan fokusera på att utforska skalbarheten hos modellerna genom att träna på en mindre del av kretsen och att extrapolera förutsägelser till hela designen verkar lovande för mer effektiv och exakt IR-falluppskattning i komplexa ASIC-designer. Dessa fördelar ger nya möjligheter inom området och utökar kapaciteten hos ML-algoritmer i uppgiften att förutsäga IR-fall.