Accelerating Cogent Confabulation: An Exploration in the Architecture Design Space

摘要

Cogent confabulation is a computation model that mimics the Hebbian learning, information storage, inter-relation of symbolic concepts, and the recall operations of the brain. The model has been applied to cognitive processing of language, audio and visual signals. This project focuses on how to accelerate the computation underlie confabulation based sentence completion through software and hardware optimization. Software implementation with appropriate data structures can improve the performance of the software by more than 5,000X. The cogent confabulation algorithm is an ideal candidate for parallel processing hardware and its performance can be significantly improved with the help of application specific, massively parallel computing platforms. However, as the complexity and parallelism of the hardware increases, cost also increases. Architectures with different performance-cost trade-offs are analyzed and compared. Our analysis shows that although increasing the number of processors or the size of memories per processor can increase performance, the hardware cost and performance improvements do not always exhibit a linear relation. Hardware configuration options must be carefully evaluated in order to achieve cost performance trade-offs.