东京大学研究人员利用MRAM架构和TGBNN算法创建节能的AI边缘IoT装置。 Tokyo University researchers create energy-efficient AI edge IoT devices using MRAM architecture and TGBNN algorithm.

东京科学大学的研究人员开发了一个基于磁内存的建筑,加强了边缘IoT装置的人工智能能力。 Researchers from the Tokyo University of Science have developed a Magnetic RAM (MRAM)-based architecture that enhances AI capabilities for edge IoT devices. 这种设计利用一种名为Ternary Gradient BNN (TGBNN) 的新训练算法,可以在保持性能的同时降低电路大小和功耗. Utilizing a new training algorithm called Ternary Gradient BNN (TGBNN), this design reduces circuit size and power consumption while maintaining performance. 创新承诺在穿戴式健康监测器和智能家庭等应用中高效的人工智能应用,通过降低能源使用量,为可持续性做出贡献。 The innovation promises efficient AI in applications like wearable health monitors and smart homes, contributing to sustainability by lowering energy usage.