Compilers, complex programs with the potential to greatly facilitate software and hardware design. This thesis focuses on enhancing the Spade hardware description language, known for its user-friendly approach to hardware design. In the realm of hardware development data size - for numerical values data size is known as "wordlength" - plays a critical role for reducing the hardware resources. This study presents an innovative approach that seamlessly integrates wordlength inference directly into the Spade language, enabling the over-estimation of numeric data sizes solely from the program's source code. The methodology involves iterative development, incorporating various smaller implementations and evaluations, reminiscent of an agile approach. To assess the efficacy of the wordlength inference, multiple place and route operations are performed on identical Spade code using various versions of nextpnr. Surprisingly, no discernible impact on hardware resource utilization emerges from the modifications introduced in this thesis. Nonetheless, the true significance of this endeavor lies in its potential to unlock more advanced language features within the Spade compiler. It is important to note that while the wordlength inference proposed in this thesis shows promise, it necessitates further integration efforts to realize its full potential.

Savannahs regulate an agro-ecosystem crucial for the production of domestic livestock, one of the main sources of income worldwide as well as in South African rural communities. Nevertheless, globally these ecosystem functions are threatened by intense human exploitation, inappropriate land use and environmental changes. Leaf area index (LAI) defined as one half the total green leaf area per unit ground surface area, is an inventory of the plant green leaves that defines the actual size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data could serve as an indicator of rangeland productivity. Consequently, the accurate and rapid estimation of LAI is a key requirement for farmers and policy makers to devise sustainable management strategies for rangeland resources. In this study, the main focus was to assess the utility and the accuracy of the PROSAILH radiative transfer model (RTM) to estimate LAI in the South African rangeland on the recently launched Landsat 8 sensor data. The Landsat 8 sensor has been a promising sensor for estimating grassland LAI as compared to its predecessors Landsat 5 to 7 sensors because of its increased radiometric resolution. For this purpose, two PROSAIL inversion methods and semi- empirical methods such as Normalized difference vegetation index (NDVI) were utilized to estimate LAI. The results showed that physically based approaches surpassed empirical approach with highest accuracy yielded by artificial neural network (ANN) inversion approach (RMSE=0.138), in contrast to the Look-Up Table (LUT) approach (RMSE=0.265). In conclusion, the results of this study proved that PROSAIL RTM approach on Landsat 8 data could be utilized to accurately estimate LAI at regional scale which could aid in rapid assessment and monitoring of the rangeland resources. / Environmental Sciences / M. Sc. (Environmental Science)

Savannahs regulate an agro-ecosystem crucial for the production of domestic livestock, one of the main sources of income worldwide as well as in South African rural communities. Nevertheless, globally these ecosystem functions are threatened by intense human exploitation, inappropriate land use and environmental changes. Leaf area index (LAI) defined as one half the total green leaf area per unit ground surface area, is an inventory of the plant green leaves that defines the actual size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data could serve as an indicator of rangeland productivity. Consequently, the accurate and rapid estimation of LAI is a key requirement for farmers and policy makers to devise sustainable management strategies for rangeland resources. In this study, the main focus was to assess the utility and the accuracy of the PROSAILH radiative transfer model (RTM) to estimate LAI in the South African rangeland on the recently launched Landsat 8 sensor data. The Landsat 8 sensor has been a promising sensor for estimating grassland LAI as compared to its predecessors Landsat 5 to 7 sensors because of its increased radiometric resolution. For this purpose, two PROSAIL inversion methods and semi- empirical methods such as Normalized difference vegetation index (NDVI) were utilized to estimate LAI. The results showed that physically based approaches surpassed empirical approach with highest accuracy yielded by artificial neural network (ANN) inversion approach (RMSE=0.138), in contrast to the Look-Up Table (LUT) approach (RMSE=0.265). In conclusion, the results of this study proved that PROSAIL RTM approach on Landsat 8 data could be utilized to accurately estimate LAI at regional scale which could aid in rapid assessment and monitoring of the rangeland resources. / Environmental Sciences / M. Sc. (Environmental Science)

Process variability is a major challenge for the design of nano scale MOSFETs due to fundamental physical limits as well as process control limitations. As the size of the devices is scales down to improve performance, the circuit becomes more sensitive to the process variations. Thus, it is necessary to have a device model that can predict the variations of device characteristics. Statistical modeling method is a potential solution for this problem. The novelty of the work is that we connect BSIM parameters directly to the underlying process parameters. This is very useful for fabs to optimize and control the specific processes to achieve certain circuit metric. This methodology and framework is extendable to any future technologies, because we used a device independent, but process depended frame work In the first part of this thesis, presents the design of nominal MOS devices with 28 nm physical gate length. The device is optimized to meet the specification of low standby power technology specification of International Technology Roadmap for Semiconductors ITRS(2012). Design of experiments are conducted and the following parameters gate length, oxide thickness, halo concentration, anneal temperature and title angle of halo doping are identified as the critical process parameters. The device performance factors saturation current, sub threshold current, output impendence and transconductance are examined under process variabilty. In the subsequent sections of the thesis, BSIM parameter extraction of MOS devices using the software ICCAP is presented. The variability of the spice parameters due to process variation is extracted. Using the extracted data a new BSIM interpolated model for a variability aware circuit design is proposed assume a single process parameter is varying. The model validation is done and error in ICCAP extraction method for process variability is less than 10% for all process variation condition in 3σ range. In the next section, proposes LUT model and interpolated method for a variability aware circuit design for single parameter variation. The error in LUT method for process variability reports less than 3% for all process variation condition in 3σ range. The error in perdition of drain current and intrinsic gain for LUT model files are very close to the result of device simulation. The focus of the work was to established effective method to interlink process and SPICE parameters under variability. This required generating a large number of BSIM parameter ducks. Since there could be some inaccuracy in large set of BSIM parameters, we used LUT as a golden standard. We used LUT modeling as a benchmark for validation of our BSIM3 model In the final section of thesis, impact of multi parameter variation of the processes in device performance is modelled using RSM method; the model is verified using ANOVA method. Models are found to be sufficient and stable. The reported error is less than 1% in all cases. Monte Carlo simulation confirms stability and repeatability of the model. The model for random variabilty of process parameters are formulated using BSIM and compared with the LUT model. The model was tested using a benchmark circuit. The maximum error in Monte Carlo simulation is found to be less than 3% for output current and less than 8% for output impedance.

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