PMID- 28832522
OWN - NLM
STAT- MEDLINE
DCOM- 20180522
LR  - 20191210
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 17
IP  - 9
DP  - 2017 Aug 23
TI  - A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in 
      a MIMO Real-Time Control System.
LID - 10.3390/s17091941 [doi]
LID - 1941
AB  - Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have 
      become the state-of-the-art methods in machine learning and achieved amazing 
      success in speech recognition, visual object recognition, and many other domains. 
      There are several hardware platforms for developing accelerated implementation of 
      ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible 
      and can provide high performance per watt of power consumption, they have drawn a 
      number of applications from scientists. In this paper, we propose a FPGA-based, 
      granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be 
      summarized as granularity variability, scalability, integrated computing, and 
      addressing ability: first, the number of neurons is variable rather than constant 
      in one core; second, the multi-core network scale can be extended in various 
      forms; third, the neuron addressing and computing processes are executed 
      simultaneously. These make the processor more flexible and better suited for 
      different applications. Moreover, a neural network-based controller is mapped to 
      FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, 
      temperature-sensing and control system. Experiments validate the effectiveness of 
      the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, 
      which is flexible, highly energy-efficient, and can be applied in many areas.
FAU - Zhang, Zhen
AU  - Zhang Z
AD  - Department of Precision Instrument, Tsinghua University, Beijing 100084, China. 
      zhangz14@mails.tsinghua.edu.cn.
FAU - Ma, Cheng
AU  - Ma C
AD  - Department of Precision Instrument, Tsinghua University, Beijing 100084, China. 
      macheng@mail.tsinghua.edu.cn.
FAU - Zhu, Rong
AU  - Zhu R
AD  - Department of Precision Instrument, Tsinghua University, Beijing 100084, China. 
      zr_gloria@mail.tsinghua.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20170823
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - *Computer Systems
MH  - Neural Networks, Computer
MH  - Neurons
PMC - PMC5620544
OTO - NOTNLM
OT  - FPGA
OT  - MIMO control
OT  - artificial neural networks
OT  - granularity variable
OT  - neuromorphic processor
COIS- The authors declare no conflict of interest.
EDAT- 2017/08/24 06:00
MHDA- 2018/05/23 06:00
PMCR- 2017/09/01
CRDT- 2017/08/24 06:00
PHST- 2017/06/27 00:00 [received]
PHST- 2017/08/15 00:00 [revised]
PHST- 2017/08/21 00:00 [accepted]
PHST- 2017/08/24 06:00 [entrez]
PHST- 2017/08/24 06:00 [pubmed]
PHST- 2018/05/23 06:00 [medline]
PHST- 2017/09/01 00:00 [pmc-release]
AID - s17091941 [pii]
AID - sensors-17-01941 [pii]
AID - 10.3390/s17091941 [doi]
PST - epublish
SO  - Sensors (Basel). 2017 Aug 23;17(9):1941. doi: 10.3390/s17091941.