University of Illinois Urbana-Champaign

Pydima: Evaluation on machine learning algorithms on deep in-memory architecture (DIMA)

Liu, Siyu

This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.

Permalink

https://hdl.handle.net/2142/104026

Description

Title
Pydima: Evaluation on machine learning algorithms on deep in-memory architecture (DIMA)
Author(s)
Liu, Siyu
Contributor(s)
Shanbhag, Naresh R.
Issue Date
2019-05
Keyword(s)
  • in-memory
  • machine learning
  • always-on inference
  • mixed-signal
  • process variations
  • circuit simulation
Abstract
While machine learning delivers state-of-the-art accuracy on many AI tasks, its performance has been limited by the cost of high energy consumption for memory bit-cell-array (BCA) usage. To address this problem, deep in-memory architecture (DIMA) has been designed to enhance both energy efficiency and throughput over conventional digital architectures by reading multiple bits per cycle and by employing mixed-signal processing in the periphery of the BCA. Due to its mixed-signal nature, DIMA is sensitive to transistor-level spatial process variation and data-dependent non-idealities, which circuit simulation tools fail to capture. In order to have an accurate simulation of DIMA, an emulator is developed based on a model which considers the discharge transistor current equation and normal distribution of spatial threshold voltage variation. The result of the emulator is compared with the measurement of a 65 nm CMOS prototype IC running support vector machine (SVM) on face detection dataset from The Center for Biological & Computational Learning (CBCL) at MIT and predicted the inference accuracy with an error range less than 2%.
Type of Resource
text
Language
en
Permalink
http://hdl.handle.net/2142/104026

Owning Collections

Senior Theses - Electrical and Computer Engineering PRIMARY
The best of ECE undergraduate research