In-memory area-efficient signal streaming processor design for binary neural networks

Yonekawa, Haruyoshi; Sato, Shusei; Nakahara, Hiroki; Ando, Keiichi; Ueyoshi, Kodai; Hirose, Kazutoshi; Orimo, Kentaro; Takamaeda-Yamazaki, Shinya; Ikebe, Masayuki; Asai, Tetsuya; Motomura, Masato

doi:10.1109/mwscas.2017.8052874

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Some of them are considering the binary approximations, choosing an implementation based on emerging technologies. Some works [12,13,26,27] are based on MTJ technology while [15][16][17][18]28,29] have used RRAM. In each of these works the resistive element is used to perform simple logical operations based on current sensing technique.…”

Section: Nn Implementations Based On Lim Conceptmentioning

confidence: 99%

“…In [26,27,30,31] several Binary Convolutional Neural Networks (BCNNs) implementations are discussed: they achieve very good results in terms of energy and power, thanks to the intrinsic low power nature of the MTJ and RRAM devices. Reference [28] proposes a BNN design based on SRAM array. The logic parts perform the computations and are disposed below the memory array.…”

Section: Nn Implementations Based On Lim Conceptmentioning

confidence: 99%

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

Coluccio

Vacca

Turvani

2020

JLPEA

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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.

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