Configurable Accelerators for Visual and Text Analytics

Open Access
Park, Mi Sun
Graduate Program:
Computer Science and Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
September 19, 2014
Committee Members:
  • Vijaykrishnan Narayanan, Dissertation Advisor
  • Mary Jane Irwin, Dissertation Advisor
  • C Lee Giles, Committee Member
  • Bradley Paul Wyble, Committee Member
  • Omesh Tickoo, Special Member
  • Accelerators
  • Recognition
  • Vision
  • Text Processing
  • Personal Analytics
Continuously recording a person's daily life and creating a digital backup of it is no longer just science fiction. Current lifelogging technologies allow us to automatically capture every single minute of a person's experience from wearable sensors. However, how to efficiently manage the captured personal multimedia data and provide user services using the data still remains a great challenge. This dissertation presents a proposal to leverage hardware acceleration for a personal analytics system that can continuously capture a person's daily life and respond user queries using the captured lifelog. First, two visual accelerators for biologically-inspired recognition are introduced: HMAX accelerator for object recognition and gist accelerator for scene recognition. This includes a description of the novel architecture, and an analysis of the experimental results of each accelerator by comparing its performance to contemporary solutions on different platforms. Second, a prototype system of personal analytics, which consists of front-end recognition and back-end text analytics, is presented to realize the vision of creating a lifelog system that enables total recall through total capture of a person's everyday experience. Third, a text accelerator for Naive Bayes multiclass classifier is presented to support the real-time text processing of the back-end analytics in personal analytics system. By leveraging these visual and text accelerators, the front-end system is able to extract essential information from multimedia data and transmit the lifelog data into the back-end system in real-time. Therefore, the analytics can perform content-aware lifelog analysis and support efficient query processing using the lifelog data.