FPGA device performance.

Currently, Convolutional Neural Networks (CNN) accelerators find application in various digital domains, each highlighting memory utilization as a significant concern leading to system degradation. In response, our present work focuses on optimizing the memory usage of CNN through a strategic approach. The resulting system is coined as the Memory Optimized Zebra CNN (MOZC). In the initial stage, the CNN accelerator is constructed with optimized features, specifically addressing the network routing function. In this context, our approach draws inspiration from zebras, aiming to identify the shortest path between network nodes. The Field-Programmable-Gate-Arrays (FPGA) are employed for evaluating MOZC performance, considering parameters such as lookup table (LUT), Flip-Flop (FF), memory utilization, power consumption, Digital-Signal Processing (DSP), and Giga-Operations-Per-Second per watt (GOPS/W). Additionally, key parameters like data delivery and Throughput assess routing and data transmission robustness. Video data is utilized to determine routing efficiency, and the achieved highest GOPS/W is recorded as 30.43, marking a substantial improvement over conventional CNN accelerators.

当前，卷积神经网络（Convolutional Neural Networks, CNN）加速器已在各类数字领域得到广泛应用，而内存利用率始终是引发系统性能退化的核心痛点之一。为此，本研究通过系统性策略聚焦卷积神经网络的内存优化问题，最终得到的优化系统被命名为内存优化型斑马卷积神经网络（Memory Optimized Zebra CNN, MOZC）。初始阶段，本研究通过优化特性构建卷积神经网络加速器，重点针对网络路由功能进行优化。在此背景下，本研究从斑马的行为模式中获取灵感，旨在求解网络节点间的最短路径。本研究采用现场可编程门阵列（Field-Programmable-Gate-Arrays, FPGA）对内存优化型斑马卷积神经网络（MOZC）的性能进行评估，评估参数涵盖查找表（lookup table, LUT）、触发器（Flip-Flop, FF）、内存利用率、功耗、数字信号处理（Digital-Signal Processing, DSP）单元以及每瓦千兆运算次数（Giga-Operations-Per-Second per watt, GOPS/W）。此外，本研究通过数据投递成功率与吞吐量两类核心参数，评估路由与数据传输的鲁棒性。本研究采用视频数据验证路由效率，最终测得的最高每瓦千兆运算次数可达30.43，较传统卷积神经网络加速器实现了显著性能提升。