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一个节能处理器阵列和内存控制器,用于精确处理基于卷积神经网络的推理引擎

S Deepika¹, V Arunachalam²

¹Department of Micro and Nanoelectronics School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.

Scientific reports

|November 12, 2025

概括

一个新的控制器有效地管理卷积神经网络 (CNNs) 完全连接 (FC) 层中的非结构化的稀疏性,在推断过程中提高能源效率. 这种硬件加速器设计改善了数据的移动,并实现了显著的性能提升.

科学领域:

计算机工程计算机工程
人工智能的人工智能
硬件加速器硬件加速器

背景情况:

利用卷积神经网络 (CNN) 硬件加速器中的非结构化的稀疏性可以提高推断的能源效率.
管理非结构化的稀疏性,特别是在完全连接 (FC) 层中,通常需要复杂的控制器来进行索引和负载平衡.

研究的目的:

设计和评估一种新的控制器,用于管理CNN的FC层中的非结构化的稀疏性.
为了提高硬件加速器的能源效率和数据传输速度,CNN的推断需要最小的硬件开销.

主要方法:

在预训练的视觉几何组-16 (VGG-16) 模型中使用诱导的稀疏性机制引入了约20%的稀疏性.
开发了一个结合IFM和权重 - 零值压缩 (CIW-ZVC) 控制器,以管理离芯片和芯片内存之间的数据移动.
使用了一个处理器数组,具有256个卷积运算符 (CO) 和平行计算,重量为零,采用基于的计算策略,具有静态输入特征图 (IFM).

主要成果:

在ImageNet数据集上实现了95%的分类准确度和0.96波精度和回忆的平均值.
CIW-ZVC控制器提高了数据移动速度,并且硬件开销最小.
14nm的实现显示了256 x 10^9操作/秒 (OPS) 的峰值性能和15 x 10^12 OPS/Watt的能量效率.
与现有处理器相比,报告的能效提高了6.08倍,面积效率提高了7.6倍.

结论:

设计的控制器有效地管理了FC层中的非结构化的稀疏性,显著提高了能源和区域效率.
拟议的硬件加速方法为基于CNN的推理提供了实质性的性能改进.
这种方法提供了一个可行的解决方案,通过解决稀疏性挑战来优化硬件加速器上的深度学习推断.

关键词:

压缩压缩压缩压缩卷积神经网络是一种卷积神经网络.数据流数据流数据流深度学习是一种深度学习.节能加速器可以节能.图像的分类图像的分类.稀缺性是一种稀缺性.

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