返回
Filtering inputs for efficient intelligence systems
DOI:10.1038/s41928-026-01698-x.png)
摘要
En 中文
通过在计算开始前决定哪些视觉输入与任务相关,可重构硬件可以减少人工智能系统中的不必要激活。
期刊
IF:
40.9
论文数:
1.7K
被引数:
2.1W
机构
引用论文
Networks of spiking neurons: The third generation of neural network models尖峰神经元网络: 第三代神经网络模型
NEURAL NETWORKS
IF6.3
A towards-foundry strategy for creating fully interconnected two-dimensional microprocessors创建完全互连的二维微处理器的迈向铸造策略
Nature Electronics
IF40.9
Ferroelectric gating of two-dimensional semiconductors for the integration of steep-slope logic and neuromorphic devices二维半导体的铁电门控,用于集成陡坡逻辑和神经形态器件
NATURE ELECTRONICS
IF40.9
Reconfigurable logic and neuromorphic circuits based on electrically tunable two-dimensional homojunctions基于电可调谐二维同质结的可重构逻辑和神经形态电路
NATURE ELECTRONICS
IF40.9
A spiking neural network-in-logic architecture based on reconfigurable molybdenum disulfide dual-gate transistors with ferroelectric gating基于可重构二硫化钼双栅晶体管与铁电栅控的逻辑内脉冲神经网络架构
Nature Electronics
IF40.9
Reconfigurable logic-in-memory architectures based on a two-dimensional van der Waals heterostructure device基于二维范德华异质结构器件的可重构逻辑存储器体系结构
NATURE ELECTRONICS
IF40.9
没有更多内容

