An efficient GPU-accelerated inference engine for binary neural network on mobile phones

AbstractThe accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.

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XOR-Net: An Efficient Computation Pipeline for Binary Neural Network Inference on Edge Devices

2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) ◽

10.1109/icpads51040.2020.00026 ◽

2020 ◽

Author(s):

Shien Zhu ◽

Luan H. K. Duong ◽

Weichen Liu

Keyword(s):

Neural Network ◽

Network Inference ◽

Efficient Computation ◽

Binary Neural Network

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Binary Neural Network as a Flexible Integrated Circuit for Odour Classification

2020 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) ◽

10.1109/fleps49123.2020.9239529 ◽

2020 ◽

Author(s):

Emre Ozer ◽

Jedrzej Kufel ◽

John Biggs ◽

James Myers ◽

Charles Reynolds ◽

...

Keyword(s):

Neural Network ◽

Integrated Circuit ◽

Binary Neural Network

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Multiplexed neurochemical transmission emulated using a dual-excitatory synaptic transistor

npj 2D Materials and Applications ◽

10.1038/s41699-021-00205-4 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Mingxue Ma ◽

Yao Ni ◽

Zirong Chi ◽

Wanqing Meng ◽

Haiyang Yu ◽

...

Keyword(s):

Neural Network ◽

Central Nervous System ◽

Nervous System ◽

Long Term Potentiation ◽

Short Term ◽

Term Potentiation ◽

Binary Neural Network ◽

Neuromorphic Systems ◽

Human Brains

AbstractThe ability to emulate multiplexed neurochemical transmission is an important step toward mimicking complex brain activities. Glutamate and dopamine are neurotransmitters that regulate thinking and impulse signals independently or synergistically. However, emulation of such simultaneous neurotransmission is still challenging. Here we report design and fabrication of synaptic transistor that emulates multiplexed neurochemical transmission of glutamate and dopamine. The device can perform glutamate-induced long-term potentiation, dopamine-induced short-term potentiation, or co-release-induced depression under particular stimulus patterns. More importantly, a balanced ternary system that uses our ambipolar synaptic device backtrack input ‘true’, ‘false’ and ‘unknown’ logic signals; this process is more similar to the information processing in human brains than a traditional binary neural network. This work provides new insight for neuromorphic systems to establish new principles to reproduce the complexity of a mammalian central nervous system from simple basic units.

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