14.7 A 288µW programmable deep-learning processor with 270KB on-chip weight storage using non-uniform memory hierarchy for mobile intelligence
By Suyoung Bang, Jingcheng Wang, Ziyun Li, Cao Gao, Yejoong Kim, Qing Dong, Yen-Po Chen, Laura Fick, Xun Sun, Ronald Dreslinski Jr. and 4 more. 2017
79 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Sheng-Chun Kao
- Ala Al-Fuqaha
- Paul Patras
- Mohsen Guizani
- Tushar Krishna
- Sameh Sorour
- Hamed Haddadi
- Chaoyun Zhang
In Electrical and Electronic Engineering
- Ala Al-Fuqaha
- M. Mohammadi
- Sheng-Chun Kao
- Paul Patras
- Mohsen Guizani
- Tushar Krishna
- Sameh Sorour
- Hamed Haddadi
Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “In addition to all prior solutions, developing tiny size processors (micromotes) with strong DL capabilities is on the rise [194] [199].”
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[155] introduce a low-power and programmable deep learning processor to deploy mobile intelligence on”
- Domain-specific Genetic Algorithm for Multi-tenant DNNAccelerator Scheduling (2021, methodology): “5 Flexible Accelerator In this experiment, we consider accelerators where the PE array dimensions are configurable, such as FPGAs [17], CGRA [57], or programmable accelerators [4, 120, 125].”