A Survey on Memory Subsystems for Deep Neural Network Accelerators

Arghavan Asad, Rupinder Kaur and Farah Mohammadi
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Arghavan Asad: Electrical and Computer Engineering Department, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3, Canada
Rupinder Kaur: Electrical and Computer Engineering Department, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3, Canada
Farah Mohammadi: Electrical and Computer Engineering Department, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3, Canada

Future Internet, 2022, vol. 14, issue 5, 1-28

Abstract: From self-driving cars to detecting cancer, the applications of modern artificial intelligence (AI) rely primarily on deep neural networks (DNNs). Given raw sensory data, DNNs are able to extract high-level features after the network has been trained using statistical learning. However, due to the massive amounts of parallel processing in computations, the memory wall largely affects the performance. Thus, a review of the different memory architectures applied in DNN accelerators would prove beneficial. While the existing surveys only address DNN accelerators in general, this paper investigates novel advancements in efficient memory organizations and design methodologies in the DNN accelerator. First, an overview of the various memory architectures used in DNN accelerators will be provided, followed by a discussion of memory organizations on non-ASIC DNN accelerators. Furthermore, flexible memory systems incorporating an adaptable DNN computation will be explored. Lastly, an analysis of emerging memory technologies will be conducted. The reader, through this article, will: 1—gain the ability to analyze various proposed memory architectures; 2—discern various DNN accelerators with different memory designs; 3—become familiar with the trade-offs associated with memory organizations; and 4—become familiar with proposed new memory systems for modern DNN accelerators to solve the memory wall and other mentioned current issues.

Keywords: deep neural network (DNN); heterogeneous architecture; in/near memory processing; memory system; reconfigurable architecture (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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