Efficient Algorithms, Hardware Architectures and Circuits for Deep Learning Accelerators

Deep learning has permeated many industries due to its state-of-the-art ability to process complex data and uncover intricate patterns. However, it is computationally expensive. Researchers have shown in theory and practice that the progress of deep learning in many applications is heavily reliant on increases in computing power, and thus leads to increasing energy demand. That may impede further advancement in the field. To tackle that challenge, this thesis presents several techniques to improve the energy efficiency of deep learning accelerators while adhering to the accuracy and throughput requirements of the desired application. First, we develop hybrid dataflows and co-design the memory hierarchy. That enables designers to trade off the reuse between different data types across different storage elements provided by the technology for higher energy efficiency. Second, we propose a weight tuning algorithm and accelerator co-design, which optimizes the bit representation of weights for energy reduction. Last, we present VideoTime3, an algorithm and accelerator co-design for efficient real-time video understanding with temporal redundancy reduction and temporal modeling. Our proposed techniques enrich accelerator designers’ toolkits, pushing the boundaries of energy efficiency for sustainable advances in deep learning.

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

2023

Author dc:creator

• Wang, Miaorong

Advisor dc:contributor.advisor

• Chandrakasan, Anantha P.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

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

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/152734

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/152734