Personalized Computer Architecture as Contextual Partitioning for Speech Recognition

TLDR: This investigation investigates the foundation of a computer architecture where processing elements and memory are contextually contextually Contextual Partitioning (CP), the situational handling of inputs, which has the potential to scale nearly linearly with increasing core counts, offering architectures effective with future processor designs.

Abstract: Computing is entering an era of hundreds to thousands of processing elements per chip, yet no known parallelism form scales to that degree. To address this problem, we investigate the foundation of a computer architecture where processing elements and memory are contextually Contextual Partitioning (CP), the situational handling of inputs, employs a method for allocating resources, novel from approaches used in on mutually exclusive parts of a task, as in Thread Level Parallelism, CP assigns different physical components to different versions of the same task, defining versions by contextual distinctions in device usage. Thus, application data is processed differently based on the situation of the user. Further, partitions may be user specific, leading to personalized architectures. Our focus is mobile devices, which are, or can be, personalized to one owner. Our investigation is centered on leveraging CP for accurate and realtime speech recognition on mobile devices, scalable to large vocabularies, a highly desired application for future user interfaces. By contextually partitioning a vocabulary, training partitions as separate acoustic models with SPHINX, we demonstrate a maximum error reduction of 61% compared to a unified approach. CP also allows for systems robust to changes in vocabulary, requiring up to 97% less training when updating old vocabulary entries with new words, and incurring fewer errors from the replacement. Finally, CP has the potential to scale nearly linearly with increasing core counts, offering architectures effective with future processor designs.