A Systematic Literature Review of Hardware Neural Networks

Parra, Dorfell; Camargo, Carlos

doi:10.1007/978-3-030-03023-0_7

Cited by 1 publication

(1 citation statement)

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“…However, recent applications of ANNs, e.g., IoT, medical systems, and telecommunication, require platforms with high throughput and the capacity to execute the algorithms in real-time. An attractive solution is the development of hardware neuronal networks (HNN) in Field-Programmable Gate Arrays (FPGAs) [15][16][17][18][19][20][21]. In this regard, the FPGA-based implementation of AFs in HNN is one of the challenges for embedded system design according to recent studies; this is because the AF implementations require low hardware resources and low power consumption [1,2,5,12,[22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Hardware-Based Activation Function-Core for Neural Network Implementations

et al. 2021

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Today, embedded systems (ES) tend towards miniaturization and the carrying out of complex tasks in applications such as the Internet of Things, medical systems, telecommunications, among others. Currently, ES structures based on artificial intelligence using hardware neural networks (HNNs) are becoming more common. In the design of HNN, the activation function (AF) requires special attention due to its impact on the HNN performance. Therefore, implementing activation functions (AFs) with good performance, low power consumption, and reduced hardware resources is critical for HNNs. In light of this, this paper presents a hardware-based activation function-core (AFC) to implement an HNN. In addition, this work shows a design framework for the AFC that applies a piecewise polynomial approximation (PPA) technique. The designed AFC has a reconfigurable architecture with a wordlength-efficient decoder, i.e., reduced hardware resources are used to satisfy the desired accuracy. Experimental results show a better performance of the proposed AFC in terms of hardware resources and power consumption when it is compared with state of the art implementations. Finally, two case studies were implemented to corroborate the AFC performance in widely used ANN applications.

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