Modern parallel computing techniques are subject to poor scalability. Their performance tends to suffer diminishing returns and even losses with increasing parallelism. Some methods of intelligent computing, such as neural networks and genetic algorithms, lend themselves well to massively parallel systems but come with other drawbacks that can limit their usefulness such as the requirement of a training phase and/or sensitivity to randomness. This thesis investigates the feasibility of a novel method of intelligent parallel computing by implementing a true multiple instruction stream, single data stream (MISD) computing system that is theoretically nearly perfectly scalable. Computational dreaming (CD) is inspired by the structure and dreaming process of the human brain. It examines previously observed input data during a 'dream phase' and is able to develop and select a simplified model to use during the day phase of computation. Using mazes as an example problem space, a CD simulator is developed and successfully used to demonstrate the viability and robustness of CD. Experiments that focused on CD viability resulted in the CD system solving 15% of mazes (ranging from small and simple to large and complex) compared with 2.2% solved by random model selection. Results also showed that approximately 50% of successful solutions generated match up with those that would be generated by algorithms such as depth first search and Dijkstra's algorithm. Experiments focusing on robustness performed repeated trials with identical parameters. Results demonstrated that CD is capable of achieving this result consistently, solving over 32% of mazes across 10 trials compared to only 3.6% solved by random model selection. A significant finding is that CD does not get stuck on local minima, always converging on a solution model. Thus, CD has the potential to enable significant contributions to computing by potentially finding elegant solutions to, for example, NP-hard or previously intractable problems. / Master of Science

Monitoring and security in multigigabit networks with speeds 1 - 100 Gb/s needs hardware acceleration. NetCOPE platform for rapid development of network applications uses hardware acceleration card with FPGA technology by means of hardware/software codesign. Increas in performance of platform's software part is dependent of parallel processing in applications to take advantage of utilising more processor cores. This thesis analyses NetCOPE platform architecture and possibilities of parallelising classic network applications and creates models of concurrent access to data in NetCOPE platform to utilize more processor cores. These models are subsequently implemented as extensions to platform's Linux system drivers. Userspace libraries are created to provide simple interface for applications to use these new features. To achieve high throughput of this solution several optimizations are performed. Results are measured by created testing tools.

Novel forms of brain-inspired programming models related to novel computer architecture are required to both understand the mysteries of intelligence as well as break barriers in computational complexity, and computer parallelism. Artificial Intelligence is focused on developing complex programs based on abstract, statistical prediction engines that require large datasets, vast amounts of computational power, and unbounded computation time. By contrast, the brain utilizes relatively few experiences to make decisions in unpredictable, time-constrained situations while utilizing relatively small amounts of physical computational space and power with high degrees of complexity and parallelism. We observe that intelligence requires the accommodation of ambiguity, conflict, and paradox. From a structural perspective, this means the same set of inputs leads to conflicting results that are likely produced in isolated regions of the brain that function independently until an answer must be chosen. We further observe that, unlike computer programs, brains constantly interact with the physical world where external constraints force the selection of the best available response in time-quality trade-offs ranging from fight-or-flight to deep thinking. For example, when intelligent beings are presented with a set of inputs, those inputs can be processed with different levels of thinking, utilizing heterogeneous algorithms to produce answers dependent upon the time available to process them. We introduce the Troop meta-approach, which is a novel meta computer architecture and programming. Experiments demonstrate our approach in modeling conflict when the same set of inputs are heterogeneously processed independently using maze solving and ordered search in real-world environments with unpredictable, random time constraints. Across one hundred trials, on average, the Troop solution solves mazes almost six times faster than the only other solution, which does not accommodate conflict but can always produce a result when required. Two other experiments based on ordered search show that, on average, the Troop solution returns a position that is over twice as accurate as the other solutions which do not accommodate conflict but always produce a result when required. This work lays the foundation for more research in algorithms that utilize time-accuracy trade-offs consistent with our approach. / This material is based upon work supported by the National Science Foundation under Grant No. 2204780. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. / Master of Science / New types of brain-inspired computer architectures and programming models are needed to break barriers that hinder traditional methods in computer parallelism as well as to understand better the phenomenon by which intelligence emerges from the structure of the human brain. Traditional research in the field of Artificial Intelligence is focused on developing complex programs based on simulating low-level models of the brain such as artificial neural networks. The most advanced of these methods are processed on large supercomputers that use vast amounts of power and have unlimited amounts of time to process a task producing a single result. By contrast, the human brain is relatively small and uses very little power. Furthermore, it can use relatively few experiences to make very quick and inaccurate but necessary decisions to survive in unpredictable environments. But the brain can produce many different and conflicting decisions to the same problem. Given more time, the human brain can use higher levels of thinking located in different parts of the brain to produce better decisions. Thus, we observe that intelligence requires the ability to handle conflicting answers to the same problem. From a highlevel perspective, this means different and independent structures of the brain can simultaneously produce conflicting answers that solve the same problem. We further observe that, unlike traditional computer programs, the brain constantly interacts with the physical world, where different circumstances within the environment force the best available decision to be carried out. Based on these observations, this research introduces novel approaches that we collectively reference as the Troop meta-approach to develop computer architectures that solve real-world problems, such as maze solving. This research demonstrates the approaches by first introducing scenarios inspired by humans solving problems in environments where unforeseeable events occur that force decisions to be made that are not the most accurate but necessary not to fail the overall objective. For example, military and law enforcement trainees use square mazes to prepare for unpredictable environments. When a threat presents itself, if a soldier or officer does not react to a circumstance in time, their failure may be fatal. To demonstrate that our approaches are feasible, this research then presents three experiments based on the problems of the scenarios and uses the Troop meta-approach to solve each one. Across three experiments, on average, the computer architectures and related algorithms developed using the Troop meta-approach solve mazes or search databases while responding to unpredictable real-world events faster or more accurately than traditional architectures and algorithm pairs that do not handle simultaneous decisions that conflict. This work lays the foundation for more research in methods and computer architectures that utilize multiple conflicting decisions.

Computer Architecture is entering an era where hundreds of Processing Elements (PE) can be integrated onto single chips even as decades-long, steady advances in instruction, thread level parallelism are coming to an end. And yet, conventional methods of parallelism fail to scale beyond 4-5 PE's, well short of the levels of parallelism found in the human brain. The human brain is able to maintain constant real time performance as cognitive complexity grows virtually unbounded through our lifetime. Our underlying thesis is that contextual categorization leading to simplified algorithmic processing is crucial to the brains performance efficiency. But, since the overheads of such reorganization are unaffordable in real time, we also observe the critical role of sleep and dreaming in the lives of all intelligent beings. Based on the importance of dream sleep in memory consolidation, we propose that it is also responsible for contextual reorganization. We target mobile device applications that can be personalized to the user, including speech, image and gesture recognition, as well as other kinds of personalized classification, which are arguably the foundation of intelligence. These algorithms rely on a knowledge database of symbols, where the database size determines the level of intelligence. Essential to achieving intelligence and a seamless user interface however is that real time performance be maintained. Observing this, we define our chief performance goal as: Maintaining constant real time performance against ever increasing algorithmic and architectural complexities. Our solution is a method for Malleable Contextual Partitioning (MCP) that enables closer personalization to user behavior. We conceptualize a novel architectural framework, the Dream Architecture for Lateral Intelligence (DALI) that demonstrates the MCP approach. The DALI implements a dream phase to execute MCP in ideal MISD parallelism and reorganize its architecture to enable contextually simplified real time operation. With speech recognition as an example application, we show that the DALI is successful in achieving the performance goal, as it maintains constant real time recognition, scaling almost ideally, with PE numbers up to 16 and vocabulary size up to 220 words. / Master of Science