Notice and Invitation

Oral Defense of Doctoral Dissertation
The Volgenau School of Engineering, George Mason University

ALI Mirzaeian 

Bachelor of Science, Isfahan University of Technology, Iran, 2012 

Master of Science, Iran University of Science and Technology, 2015 


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: 

All are invited to attend. 



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.