Cho Seung-Myeong, Lee Jaesung, Jo Hyejin, Yun Dai, Moon Jihwan, Min Kyeong-Sik
School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.
Nanomaterials (Basel). 2025 Jul 28;15(15):1166. doi: 10.3390/nano15151166.
Artificial intelligence (AI) has become ubiquitous in modern computing systems, from high-performance data centers to resource-constrained edge devices. As AI applications continue to expand into mobile and IoT domains, the need for energy-efficient neural network implementations has become increasingly critical. To meet this requirement of energy-efficient computing, this work presents a BWNN (binary-weighted neural network) architecture implemented using FeRAM (Ferroelectric RAM)-based synaptic arrays. By leveraging the non-volatile nature and low-power computing of FeRAM-based CIM (computing in memory), the proposed CIM architecture indicates significant reductions in both dynamic and standby power consumption. Simulation results in this paper demonstrate that scaling the ferroelectric capacitor size can reduce dynamic power by up to 6.5%, while eliminating DRAM-like refresh cycles allows standby power to drop by over 258× under typical conditions. Furthermore, the combination of binary weight quantization and in-memory computing enables energy-efficient inference without significant loss in recognition accuracy, as validated using MNIST datasets. Compared to prior CIM architectures of SRAM-CIM, DRAM-CIM, and STT-MRAM-CIM, the proposed FeRAM-CIM exhibits superior energy efficiency, achieving 230-580 TOPS/W in a 45 nm process. These results highlight the potential of FeRAM-based BWNNs as a compelling solution for edge-AI and IoT applications where energy constraints are critical.
人工智能（AI）在现代计算系统中已无处不在，从高性能数据中心到资源受限的边缘设备。随着人工智能应用不断扩展到移动和物联网领域，对节能神经网络实现的需求变得越来越关键。为满足这种节能计算的要求，这项工作提出了一种使用基于铁电随机存取存储器（FeRAM）的突触阵列实现的二进制加权神经网络（BWNN）架构。通过利用基于FeRAM的内存计算（CIM）的非易失性和低功耗计算特性，所提出的CIM架构在动态和待机功耗方面均有显著降低。本文的仿真结果表明，缩小铁电电容器尺寸可使动态功耗降低高达6.5%，而消除类似动态随机存取存储器（DRAM）的刷新周期可使典型条件下的待机功耗降低超过258倍。此外，二进制权重量化和内存计算的结合能够实现节能推理，且识别准确率不会有显著损失，这已通过MNIST数据集得到验证。与先前的静态随机存取存储器 - CIM（SRAM - CIM）、动态随机存取存储器 - CIM（DRAM - CIM）和自旋转移力矩磁随机存取存储器 - CIM（STT - MRAM - CIM）架构相比，所提出的FeRAM - CIM具有更高的能效，在45纳米工艺中可实现230 - 580万亿次操作每秒每瓦（TOPS/W）。这些结果凸显了基于FeRAM的BWNN作为边缘人工智能和物联网应用极具吸引力的解决方案的潜力，在这些应用中能源限制至关重要。
