Modern computer application require large amounts of data processing. Traditional computing models involve constant data transfer between memory and processor. This data transfer is a major contributor to high energy consumption. As these applications scale, the energy demand increases. This poses challenges in terms of sustainability and operational costs. Computation In Memory (CIM) integrates processing within the memory. This reduces the need for data transfer between memory and processor. Potential for drastically lowering energy consumption.

CIM macros are often implemented using modified SRAM cells, though recent literature explores memristor-based CIM designs due to the memristor’s low-energy, non-volatile characteristics. However, no comprehensive comparisons between SRAM-based and memristor-based CIM designs exist. While memristor-based designs are hypothesized to be more energy-efficient, this has not yet been proven.

This thesis compares SRAM-based and memristor-based CIM designs to determine which is better suited for CIM applications. This has been achieved by exploring the state of the art of memristive devices, memristor based CIM macros and SRAM based CIM macros. A selection of designs were chosen to compare, including the 1T1R and 8T SRAM design, which are the most popular memristor based and SRAM based CIM designs. The schematics of all the designs were recreated and simulated using as much of the same parameters as possible in all of the designs. A simulation of performing the logic AND and the MAC operation was made. Additionally a layout of the designs was made to extract the area. The designs were compared based on area, energy consumption and delay.

From the results could be concluded that the best device for CIM depends on the application. The memristor design had the smallest area and consumed the least amount of energy for reading, logic and MAC operations. The memristor design also consumed the most amount of energy during writing and the delay for all operations is longer than with the SRAM based designs. If area, energy consumption and delay are equally important for an application, then memristor based CIM would be the better choice only if there are much more logic/read operations than write operations. It could be the better choice for MAC operations if a more energy efficient ADC was used than the one used in this thesis.

At virtually every public venue, announcements for visitors are made via a public addressing system. It is important that announcements transmitted by means of a public addressing system are not only audible, but also well understood by the general public. This thesis covers one of three subsystems required to make
a product that is capable of improving intelligibility of speech based on noise statistics in a room. Moreover, these statistics allow for an automatic volume control in order for listeners to experience an improved audio level. Therefore, this thesis aims at estimating the noise statistics in real time, while prior knowledge on the distorting announcement signal is available. Consequently, the concept of adaptive filtering deems suiting and is extensively studied. In order to meet the real time processing constraint, members of least mean squares algorithms are examined. Therefore, the method of steepest decent is covered, leading to the expounding of the traditional least mean squares (LMS) algorithm and the normalized LMS (NLMS) algorithm.
By assessment of the algorithms regarding different step sizes and filter lengths, the NLMS algorithm is shown to be superior in terms of faster convergence speed and better stability characteristics considering similar conditions for both algorithms. Subsequently, the results show that the NLMS is able to estimate the
noise in noise-to-signal ratio’s higher than -15dB. Also, its low complexity allows it to be suitable for real time applications, hence meeting the requirements.