Notice and Invitation
Oral Defense of Doctoral Dissertation
The Volgenau School of Engineering, George Mason University
A CNN/MLP Neural Processing Engine, Powered by Novel Temporal-Carry-Deferring MACs
Friday July 23, 3:30 PM – 5:00 PM
Zoom Meeting Link: https://gmu.zoom.us/j/92637646611
Dr. Avesta Sasan, Chair
Dr. Zhi Tian
Dr. Liang Zhao
Dr. Sai Manoj
The applications of machine learning algorithms are innumerable and cover nearly every domain of modern technology. However, as machine learning has so far required a power source with more capacity and higher efficiency than
a conventional battery. Therefore, introducing neural network accelerators with low energy demands and low latency for executing machine learning techniques has
drawn lots of attention in both the academia and industry.
In this work, we first propose the design of Temporal-Carry-deferring MAC (TCD-MAC) and illustrate how our proposed solution can gain significant energy and performance benefit when utilized to process a stream of input data.
We then propose using the TCD-MAC to build a reconfigurable, high speed, and low power Neural Processing Engine (TCD-NPE). Furthermore, we expand the idea of TCD-MAC to present NESTA, which is a specialized Neural engine that reformats Convolutions into
$3 \times 3$ batches and uses a hierarchy of Hamming Weight Compressors to process each batch.