吞吐量
卷积神经网络
硬件加速
深度学习
计算机科学
人工神经网络
计算机硬件
循环神经网络
嵌入式系统
机器学习
计算机体系结构
现场可编程门阵列
人工智能
操作系统
无线
作者
Tamador Mohaidat,Kasem Khalil
出处
期刊:IEEE transactions on artificial intelligence
[Institute of Electrical and Electronics Engineers]
日期:2024-03-14
卷期号:5 (8): 3801-3822
被引量:10
标识
DOI:10.1109/tai.2024.3377147
摘要
Artificial intelligence hardware accelerator is an emerging research for several applications and domains. The hardware accelerator's direction is to provide high computational speed with retaining low-cost and high learning performance. The main challenge is to design complex machine learning models on hardware with high performance. This paper presents a thorough investigation into machine learning accelerators and associated challenges. It describes a hardware implementation of different structures such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Artificial Neural Network (ANN). The challenges such as speed, area, resource consumption, and throughput are discussed. It also presents a comparison between the existing hardware design. Lastly, the paper describes the evaluation parameters for a machine learning accelerator in terms of learning & testing performance and hardware design.
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