4 accepted papers
It is well known that Winograd convolution algorithms speed up the widely used small-size convolutions. However, the problem of quantization of Winograd convolutions is challenging - while quantization of slower Winograd algorithms does not cause problems, quantization of faster Winograd algorithms…
Deep Neural Networks (DNNs) usually have a large number of parameters and consume a huge volume of storage space, which limits the application of DNNs on memory-constrained devices. Network quantization is an appealing way to compress DNNs. However, most of existing quantization methods require the…
"While Deep Neural Networks (DNNs) quantization leads to a significant reduction in computational and storage costs, it reduces model capacity and therefore, usually leads to an accuracy drop. One of the possible ways to overcome this issue is to use different quantization bit-widths for different l…
"Neural network quantization techniques have been a prevailing way to reduce the inference time and storage cost of full-precision models for mobile devices. However, they still suffer from accuracy degradation due to inappropriate gradients in the optimization phase, especially for low-bit precisio…