In this post, I will introduce several deep learning package which can be delpoyed to mobile platforms.
Java 9 has been release. Let’s check the most important updates:
XDA has made a comparison of speed, thermal and performance for a list of fast charge standards. You can access the full content here
I have created a list of phone which I am interested to buy.
Open-source GUINNESS makes FPGA-accelerated, binarized neural networks easy to pour right from the SDSoC tap
A new open-source tool named GUINNESS makes it easy for you to develop binarized (2-valued) neural networks (BNNs) for Zynq SoCs and Zynq UltraScale+ MPSoCs using the SDSoC Development Environment. GUINNESS is a GUI-based tool that uses the Chainer deep-learning framework to train a binarized CNN. In a paper titled On-Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA presented at the recent 2017 IEEE International Parallel and Distributed Processing Symposium Workshops, authors Haruyoshi Yonekawa and Hiroki Nakahara describe a system they developed to implement a binarized CNN for the VGG-16 benchmark on the Xilinx ZCU102 Eval Kit, which is based on a Zynq UltraScale+ ZU9EG MPSoC. Nakahara presented the GUINNESS tool again this week at FPL2017 in Ghent, Belgium.</p>
Alexander Zlatkov has written a nice article how does the memory work in
Java, thus user doesn’t need to explicitly handle the memory allocation and release.