Devices and Algorithms for Analog Deep Learning

Efforts to realize analog processors have skyrocketed over the last decade as having energy-efficient deep learning accelerators became imperative for the future of information processing. However, the absence of two entangled components creates an impasse before their practical implementation: devices satisfying algorithm-imposed requirements and algorithms running on nonideality-tolerant routines. This thesis demonstrates a near-ideal device technology and a superior neural network training algorithm that can ultimately propel analog computing when combined together. The CMOS-compatible nanoscale protonic devices demonstrated here show unprecedented characteristics, incorporating the benefits of nanoionics with extreme acceleration of ion transport and reactions under strong electric fields. Enabled by a material-level breakthrough of utilizing phosphosilicate glass (PSG) as a proton electrolyte, this operation regime achieves controlled shuttling and intercalation of protons in nanoseconds at room temperature in an energy-efficient manner. Then, a theoretical analysis is carried out to explain the infamous incompatibility between asymmetric device modulation and conventional neural network training algorithms. By establishing a powerful analogy with classical mechanics, a novel method, Stochastic Hamiltonian Descent, is developed to exploit device asymmetry as a useful feature. Overall, devices and algorithms developed in this thesis have immediate applications in analog deep learning, whereas the overarching methodology provides further insight for future advancements.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Onen, O. Murat

Advisor dc:contributor.advisor

• del Alamo, Jesús A.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144582

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144582