Recently, the Logic-in-Memory (LiM) concept has been widely studied in the literature. This paradigm represents one of the most efficient ways to solve the limitations of a Von Neumann’s architecture: by placing simple logic circuits inside or near a memory element, it is possible to obtain a local computation without the need to fetch data from the main memory. Although this concept introduces a lot of advantages from a theoretical point of view, its implementation could introduce an increasing complexity overhead of the memory itself, leading to a more sophisticated design flow. As a case study, Binary Neural Networks (BNNs) have been chosen. BNNs binarize both weights and inputs, transforming multiply-and-accumulate into a simpler bitwise logical operation while maintaining high accuracy, making them well-suited for a LiM implementation. In this paper, we present two circuits implementing a BNN model in CMOS technology. The first one, called Out-Of-Memory (OOM) architecture, is implemented following a standard Von Neumann structure. The same architecture was redesigned to adapt the critical part of the algorithm for a modified memory, which is also capable of executing logic calculations. By comparing both OOM and LiM architectures we aim to evaluate if Logic-in-Memory paradigm is worth it. The results highlight that LiM architectures have a clear advantage over Von Neumann architectures, allowing a reduction in energy consumption while increasing the overall speed of the circuit.
Logic-in-Memory Computation: Is It Worth it? A Binary Neural Network Case Study / Coluccio, Andrea; Vacca, Marco; Turvani, Giovanna. - In: JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS. - ISSN 2079-9268. - 10:1(2020), p. 7. [10.3390/jlpea10010007]
Titolo: | Logic-in-Memory Computation: Is It Worth it? A Binary Neural Network Case Study | |
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Data di pubblicazione: | 2020 | |
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Digital Object Identifier (DOI): | http://dx.doi.org/10.3390/jlpea10010007 | |
Appare nelle tipologie: | 1.1 Articolo in rivista |
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http://hdl.handle.net/11583/2797402