MEMRISTIVE ANALOG MEMORY FOR DEEP NEURAL NETWORK ARCHITECTURES
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Date
2022-03-04
Authors
Irmanova, Aidana
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Analog switching memristive devices can be used as part of the acceleration block
of Neural Network circuits or the part of the building block of neuromorphic architectures
at the edge solutions. Ideally, memristive devices are nanoscale, low-power resistive
devices that can store an analog continuum of resistive levels with a predictable
and stable mechanism of switching. Practically, memristors can be fabricated at the
nano-scale, and operate at low power, however, the current state of the art of memristor
technology hinders the widespread adoption of memristive neuromorphic circuits due
to the system level and device level issues. The device-level issues include the variability
aspects of the switching mechanism and endurance-related limitations. As analog
resistive switching is a fragile process, memristors are prone to early aging, precision
loss, and variability or errors. System-level issues include the design of the architecture
of Neural Network as well as training schemes for fast convergence with the given
limitations of the hardware resources. The memory resources are limited by the quantized
analog levels of memristors. In this work, the memristors are used to build an
analog memory for deep neural network architectures. For efficient use of memristive
devices in neuromorphic circuits, it is required to control the resistive switching
process of memristors. Controlling resistive switching enables both off and on-chip
training prospects of neural network optimization. In addition, the controlling resistive
switching process can be used to spot the malfunctions in the stack of memristor arrays.
Such detection methods, on and off-chip training issues as well as quantizing the analog
levels of memristive states are discussed in this work.
Description
Keywords
Memristive memory, Memristor Crossbar Array, Analog Memory, Programming Analog Memory, ANN, HTM, AI, Type of access: Gated Access
Citation
Irmanova, A. (2021). Memristive Analog Memory for Deep Neural Network Architectures (Unpublished PhD thesis). Nazarbayev University, Nur-Sultan, Kazakhstan