Comments (2)
yanghaojin
commented on May 10, 2024
2
Hi,
thank you for your comment, we will continue this work to further improve the performance, and also consider to compare with the fast convolution algorithm (maybe we could also take advantages
with it). In this implementation we actually paid more attention on how to reduce the model size and enable the application of complicated deep models on mobile devices; and how to make the binary components be used as easy as possible. Of course further speedup is also our next goal.
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dongxiao92
commented on May 10, 2024
thank you for your reply.cheers~
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Related Issues (20)
- Error while doing inference on binarized model. HOT 12
- What is the location of the implement of QActivation, QConvolution, and QFullyConneted? HOT 2
- encounter errors durring complie HOT 2
- cudnn_off=False has some problems with mx.mon.Monitor HOT 4
- Where do you implement XNOR networks? HOT 1
- Build failing
- How to integrate a trained binary model in iOS? HOT 3
- No difference in binarized and full-precision LeNet model size HOT 2
- Can an existing MXNet 1.0.0 installation be used to set up BMXNet ? HOT 10
- Order of QActivation, QConvolution layers HOT 1
- Quantization method HOT 3
- depth-wise convolution support HOT 2
- less forward speed-up when batch size is larger HOT 1
- mxnet error: redeclaration of 'kNone' when setting USE_CPP_PACKAGE = 1 HOT 1
- Any pre-built dynamic library or executable file for andriod? HOT 2
- No speedup or even slower on CPU with 1-bit QConvolution and QActivation HOT 4
- ImportError: cannot import name 'transforms' HOT 2
- How do I get the model-convertor executable file ?
- XNOR-Net implementation HOT 1
- Regarding pretrained binary models
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