Malleable Contextual Partitioning and Computational Dreaming
Brar, Gurkanwal Singh
MetadataShow full item record
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.
- Masters Theses 
Showing items related by title, author, creator and subject.
Khalifa, Ahmed Abdelmonem Abuelfotooh Ali (Virginia Tech, 2015-03-11)We are witnessing exponential growth in the number of powerful, multiply-connected, energy-rich stationary and mobile nodes, which will make available a massive pool of computing and communication resources. We claim that ...
The Attitudes of African American Middle School Girls Toward Computer Science: Influences of Home, School, and Technology Use Robinson, Ashley Renee (Virginia Tech, 2015-05-13)The number of women in computing is significantly low compared to the number of men in the discipline, with African American women making up an even smaller segment of this population. Related literature accredits this ...
Abrams, Gregory; Adhinarayanan, Vignesh; Feng, Wu-chun; Rogers, David; Ahrens, Jams; Wilson, Luke (Department of Computer Science, Virginia Polytechnic Institute & State University, 2017-09-29), TR-17-05As high-performance computing (HPC) moves towards the exascale era, large-scale scientific simulations are generating enormous datasets. A variety of techniques (e.g., in-situ methods, data sampling, and compression) have ...