增强的卷积神经网络加速器,具有用于路由应用程序的内存优化
Srikanth Prasad Nallabelli1, Sundar Sampath1
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
PloS one
|April 21, 2025
概括
这项研究介绍了Memory Optimized Zebra CNN (MOZC),一个高效的卷积神经网络 (CNN) 加速器. MOZC显著提高了内存利用率和性能,在增强的数字应用中实现了高的GOPS/W.
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:
- 在卷积神经网络 (CNN) 加速器中,内存利用是一个关键的挑战,往往导致系统性能恶化.
- 现有的CNN加速器在高效的内存管理和数据路由方面存在局限性.
- 优化内存使用对于提高CNN在数字系统中的整体效率和适用性至关重要.
研究的目的:
- 开发一种新的CNN加速器,即内存优化斑马CNN (MOZC),重点是优化内存利用率.
- 通过采用以斑马条纹模式为灵感的最短路径策略来改进CNN加速器内的网络路由功能.
- 评估拟议的MOZC系统在现场可编程门阵列 (FPGA) 上的性能和效率.
主要方法:
- 设计了一个CNN加速器,结合了受斑马模式启发的优化网络路由功能,以找到节点之间的最短路径.
- 实现了内存优化斑马CNN (MOZC) 系统.
- 在现场可编程门阵列 (FPGA) 上评估MOZC性能,测量包括LUT,FF,内存利用率,功耗,DSP使用率和每瓦每秒千兆运算 (GOPS/W) 的指标.
- 使用视频数据的数据传输和吞吐量参数评估路由和数据传输的稳定性.
主要成果:
- 与传统的CNN加速器相比,MOZC系统在内存利用和整体性能方面取得了显著的改进.
- 实现了最高的GOPS/W30.43,表明能源效率取得了实质性的收益.
- 使用视频数据验证了路由效率和数据传输稳定性,证实了有效的数据传输和吞吐量.
- FPGA评估显示了优化资源利用,包括查找表 (LUT),翻页 (FF) 和数字信号处理 (DSP) 块.
结论:
- 记忆优化斑马CNN (MOZC) 有效地解决了CNN加速器中的记忆利用挑战.
- 以斑马为灵感的路由策略提高了效率和稳定性,导致了优越的性能指标.
- MOZC代表了在开发高性能和内存效率高的CNN硬件加速器方面取得的重大进步,特别是在FPGA实现方面.
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