Loihi: A Neuromorphic Manycore Processor with On-Chip Learning

被引：2414

作者：

Davies, Mike ^{[1
]}

Srinivasa, Narayan ^{[2
]}

Lin, Tsung-Han ^{[1
]}

Chinya, Gautham ^{[1
]}

Cao, Yongqiang ^{[1
]}

Choday, Sri Harsha ^{[1
]}

Dimou, Georgios ^{[3
]}

Joshi, Prasad ^{[1
]}

Imam, Nabil ^{[1
]}

Jain, Shweta ^{[1
]}

Liao, Yuyun ^{[1
]}

Lin, Chit-Kwan ^{[1
]}

Lines, Andrew ^{[1
]}

Liu, Ruokun ^{[1
]}

Mathaikutty, Deepak ^{[1
]}

Mccoy, Steve ^{[1
]}

Paul, Arnab ^{[1
]}

Tse, Jonathan ^{[1
]}

Venkataramanan, Guruguhanathan ^{[1
]}

Weng, Yi-Hsin ^{[1
]}

Wild, Andreas ^{[1
]}

Yang, Yoonseok ^{[1
]}

Wang, Hong ^{[1
]}

机构：

[1] Intel Corp, Intel Labs, Santa Clara, CA 95054 USA

[2] Eta Compute, Westlake Village, CA 91362 USA

[3] Reduced Energy Microsyst, San Francisco, CA USA

来源：

IEEE MICRO | 2018年 / 38卷 / 01期

关键词：

artificial intelligence; machine learning; neuromorphic computing;

D O I：

10.1109/MM.2018.112130359

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Loihi is a 60-mm(2) chip fabricated in Intel's 14-nm process that advances the state-of-the-art modeling of spiking neural networks in silicon. It integrates a wide range of novel features for the field, such as hierarchical connectivity, dendritic compartments, synaptic delays, and, most importantly, programmable synaptic learning rules. Running a spiking convolutional form of the Locally Competitive Algorithm, Loihi can solve LASSO optimization problems with over three orders of magnitude superior energy-delay product compared to conventional solvers running on a CPU isoprocess/voltage/area. This provides an unambiguous example of spike-based computation, outperforming all known conventional solutions.

引用

页码：82 / 99

页数：18

共 14 条

[1] A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [J].