23 citations · 60 across the 6 of their papers we have counts for
6 papers
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
Yuhang Li, Mingzhu Shen, Jian Ma +6
Model quantization has emerged as an indispensable technique to accelerate deep learning inference. While researchers continue to push the frontier of quantization algorithms, exis…
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment
Qigong Sun, Xiufang Li, Yan Ren +4
As an effective technique to achieve the implementation of deep neural networks in edge devices, model quantization has been successfully applied in many practical applications. No…
MWQ: Multiscale Wavelet Quantized Neural Networks
Qigong Sun, Yan Ren, Licheng Jiao +3
Model quantization can reduce the model size and computational latency, it has become an essential technique for the deployment of deep neural networks on resourceconstrained hardw…
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization
Qigong Sun, Licheng Jiao, Yan Ren +3
Since model quantization helps to reduce the model size and computation latency, it has been successfully applied in many applications of mobile phones, embedded devices and smart…
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
Qigong Sun, Fanhua Shang, Kang Yang +3
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance. Thus, DNNs cannot be efficiently applied to mobile phones…
ASVRG: Accelerated Proximal SVRG
Fanhua Shang, Licheng Jiao, Kaiwen Zhou +3
This paper proposes an accelerated proximal stochastic variance reduced gradient (ASVRG) method, in which we design a simple and effective momentum acceleration trick. Unlike most…