Electrochemical random access memory as deep learning accelerators
Cui, Jinsong
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/2142/127349
Description
Title
Electrochemical random access memory as deep learning accelerators
Author(s)
Cui, Jinsong
Issue Date
2024-12-05
Director of Research (if dissertation) or Advisor (if thesis)
Cao, Qing
Doctoral Committee Chair(s)
Cao, Qing
Committee Member(s)
Zuo, Jian-min
Ghose, Saugata
Zhang, Yingjie
Department of Study
Materials Science & Engineerng
Discipline
Materials Science & Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
ECRAM, deep learning, semiconductor, CMOS, Neuromorphics, Training, Nonvolatile memory, Programming
Abstract
The ever-expanding capabilities of machine learning are powered by exponentially growing complexity of deep neural network (DNN) models, requiring more energy and chip-area efficient hardware to carry out increasingly computational expensive model-inference and training tasks. Electrochemical random-access memories (ECRAMs) are developed specifically to implement efficient analog in-memory computing for these data-intensive workloads, showing some critical advantages over competing memory technologies mostly developed originally for digital electronics. In this Ph.D. dissertation, we investigated energy efficient electronic devices based on a CMOS compatible ECRAM consisting of WO3 matrix and proton carrier. The system has capability to switch between a very large number of memristive states with a high level of symmetry, small cycle-to-cycle variability, and low energy consumption; and they simultaneously exhibit good endurance, long data retention, fast switching speed.
We have designed and demonstrated a novel ECRAM architecture featuring layers of channel material, solid-state electrolyte, and a dedicated ion-storage compartment. Traditional ECRAMs often face a trade-off between switching speed and CMOS compatibility. To achieve faster switching speeds, smaller ions such as Li+ have typically been used, prioritizing speed but complicating fabrication processes. Conversely, using O2- ions enhances CMOS compatibility, though at the cost of reduced switching speeds due to the larger size of the oxygen ion. In our study, we have utilized protons to simultaneously address both issues. Our ECRAM, constructed with CMOS-compatible metal oxides, operates by shuffling protons. Moreover, with a symmetric gate stack, the device can be controlled with voltage pulses, allowing for high-speed and symmetric analog programming with minimal variability. This analog ECRAM serves as a foundational block for in-memory computing, significantly reducing energy consumption and latency in AI training and inference tasks.
We also demonstrate monolithic integration of ECRAM with silicon transistors to form pseudo-crossbar arrays. These arrays are crucial as they enable the execution of matrix multiplication directly on the ECRAM array, marking a significant advancement in hardware design. The importance of pseudo-crossbar arrays extends to their capacity to facilitate both training and inference phases of deep neural networks, directly within the memory. This integration significantly boosts computational efficiency and reduces the energy costs associated with data movement between processors and memory in traditional architectures. Our work represents the first physical demonstration of performing matrix multiplication on an ECRAM array, setting a new benchmark for in-memory computing technologies and their application in advanced AI systems.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.