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Article

Logic-in-Memory Computation: Is It Worth It? A Binary Neural Network Case Study

Department of electronics and telecommunication (DET), Politecnico di Torino, Corso Castelfidardo 39, 10129 Torino, Italy
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J. Low Power Electron. Appl. 2020, 10(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/jlpea10010007
Received: 23 December 2019 / Revised: 29 January 2020 / Accepted: 4 February 2020 / Published: 22 February 2020
(This article belongs to the Special Issue Low Power Memory/Memristor Devices and Systems)
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. View Full-Text
Keywords: Logic-in-Memory (LiM); Von Neumann’s bottleneck; memory-wall Logic-in-Memory (LiM); Von Neumann’s bottleneck; memory-wall
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MDPI and ACS Style

Coluccio, A.; Vacca, M.; Turvani, G. Logic-in-Memory Computation: Is It Worth It? A Binary Neural Network Case Study. J. Low Power Electron. Appl. 2020, 10, 7. https://0-doi-org.brum.beds.ac.uk/10.3390/jlpea10010007

AMA Style

Coluccio A, Vacca M, Turvani G. Logic-in-Memory Computation: Is It Worth It? A Binary Neural Network Case Study. Journal of Low Power Electronics and Applications. 2020; 10(1):7. https://0-doi-org.brum.beds.ac.uk/10.3390/jlpea10010007

Chicago/Turabian Style

Coluccio, Andrea, Marco Vacca, and Giovanna Turvani. 2020. "Logic-in-Memory Computation: Is It Worth It? A Binary Neural Network Case Study" Journal of Low Power Electronics and Applications 10, no. 1: 7. https://0-doi-org.brum.beds.ac.uk/10.3390/jlpea10010007

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