计算机科学
透视图(图形)
人工神经网络
电路设计
算法
计算机体系结构
并行计算
人工智能
嵌入式系统
作者
Abhishek Moitra,Abhiroop Bhattacharjee,Yuhang Li,Youngeun Kim,Priyadarshini Panda
出处
期刊:Applied physics reviews
[American Institute of Physics]
日期:2024-09-01
卷期号:11 (3)
摘要
This review explores the intersection of bio-plausible artificial intelligence in the form of spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain, highlighting their collective potential for low-power edge computing environments. Through detailed investigation at the device, circuit, and system levels, we highlight the pivotal synergies between SNNs and IMC architectures. Additionally, we emphasize the critical need for comprehensive system-level analyses, considering the inter-dependencies among algorithms, devices, circuit, and system parameters, crucial for optimal performance. An in-depth analysis leads to the identification of key system-level bottlenecks arising from device limitations, which can be addressed using SNN-specific algorithm–hardware co-design techniques. This review underscores the imperative for holistic device to system design-space co-exploration, highlighting the critical aspects of hardware and algorithm research endeavors for low-power neuromorphic solutions.
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