路由器
神经形态工程学
计算机科学
异步通信
静态随机存取存储器
加法器
高效能源利用
计算机体系结构
多核处理器
泄漏(经济)
炸薯条
嵌入式系统
并行计算
计算机硬件
延迟(音频)
工程类
人工神经网络
电气工程
计算机网络
电信
机器学习
经济
宏观经济学
作者
Vishnu P. Nambiar,Junran Pu,Yun Kwan Lee,Aarthy Mani,E. K. Koh,Ming Ming Wong,Fei Li,Wang Ling Goh,Anh Tuan
出处
期刊:IEEE Transactions on Circuits and Systems Ii-express Briefs
[Institute of Electrical and Electronics Engineers]
日期:2021-07-13
卷期号:68 (9): 3148-3152
被引量:2
标识
DOI:10.1109/tcsii.2021.3096883
摘要
This brief presents a neuromorphic processor with asynchronous routers and configurable LIF neuron models. The neurocore microarchitecture revolves around a high- V th SRAM to reduce leakage, alongside reconfigurable neuron compute logic circuits and async routers to maximize energy efficiency. The neuron compute module achieves low power via an area efficient ALU implementation by using only adder and bitshifter circuits. We describe this LIF neuron model ALU design, and also include key neurocore verification scenarios (i.e., router deadlocks and functional coverage), CPU-neurocore control flow, and asynchronous router performance analysis. Our 16-core fabricated chip in 40 nm CMOS process works down to 0.5V. The measured leakage and average energy efficiency are 0.93 μW/core and 4.8 pJ/SOP respectively (at 0.5V), which is 20% better than state of the art.
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