Search Results for author: Qing Jin

Found 11 papers, 6 papers with code

PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems

no code implementations18 Sep 2022 Qing Jin, Zhiyu Chen, Jian Ren, Yanyu Li, Yanzhi Wang, Kaiyuan Yang

In this paper, we propose a method for training quantized networks to incorporate PIM quantization, which is ubiquitous to all PIM systems.

Quantization

F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

1 code implementation ICLR 2022 Qing Jin, Jian Ren, Richard Zhuang, Sumant Hanumante, Zhengang Li, Zhiyu Chen, Yanzhi Wang, Kaiyuan Yang, Sergey Tulyakov

Our approach achieves comparable and better performance, when compared not only to existing quantization techniques with INT32 multiplication or floating-point arithmetic, but also to the full-precision counterparts, achieving state-of-the-art performance.

Quantization

MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

1 code implementation NeurIPS 2021 Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, Siyue Wang, Minghai Qin, Bin Ren, Yanzhi Wang, Sijia Liu, Xue Lin

Systematical evaluation on accuracy, training speed, and memory footprint are conducted, where the proposed MEST framework consistently outperforms representative SOTA works.

CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference

no code implementations6 Jul 2021 Zhiyu Chen, Zhanghao Yu, Qing Jin, Yan He, Jingyu Wang, Sheng Lin, Dai Li, Yanzhi Wang, Kaiyuan Yang

A compact, accurate, and bitwidth-programmable in-memory computing (IMC) static random-access memory (SRAM) macro, named CAP-RAM, is presented for energy-efficient convolutional neural network (CNN) inference.

Teachers Do More Than Teach: Compressing Image-to-Image Models

1 code implementation CVPR 2021 Qing Jin, Jian Ren, Oliver J. Woodford, Jiazhuo Wang, Geng Yuan, Yanzhi Wang, Sergey Tulyakov

In this work, we aim to address these issues by introducing a teacher network that provides a search space in which efficient network architectures can be found, in addition to performing knowledge distillation.

Knowledge Distillation

Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?

no code implementations19 Feb 2021 Ning Liu, Geng Yuan, Zhengping Che, Xuan Shen, Xiaolong Ma, Qing Jin, Jian Ren, Jian Tang, Sijia Liu, Yanzhi Wang

In deep model compression, the recent finding "Lottery Ticket Hypothesis" (LTH) (Frankle & Carbin, 2018) pointed out that there could exist a winning ticket (i. e., a properly pruned sub-network together with original weight initialization) that can achieve competitive performance than the original dense network.

Model Compression

FracBits: Mixed Precision Quantization via Fractional Bit-Widths

1 code implementation4 Jul 2020 Linjie Yang, Qing Jin

Model quantization helps to reduce model size and latency of deep neural networks.

Quantization

Towards Efficient Training for Neural Network Quantization

2 code implementations21 Dec 2019 Qing Jin, Linjie Yang, Zhenyu Liao

To deal with this problem, we propose a simple yet effective technique, named scale-adjusted training (SAT), to comply with the discovered rules and facilitates efficient training.

Quantization

AdaBits: Neural Network Quantization with Adaptive Bit-Widths

1 code implementation CVPR 2020 Qing Jin, Linjie Yang, Zhenyu Liao

With our proposed techniques applied on a bunch of models including MobileNet-V1/V2 and ResNet-50, we demonstrate that bit-width of weights and activations is a new option for adaptively executable deep neural networks, offering a distinct opportunity for improved accuracy-efficiency trade-off as well as instant adaptation according to the platform constraints in real-world applications.

Quantization

Rethinking Neural Network Quantization

no code implementations25 Sep 2019 Qing Jin, Linjie Yang, Zhenyu Liao

To deal with this problem, we propose a simple yet effective technique, named scale-adjusted training (SAT), to comply with the discovered rules and facilitates efficient training.

Quantization

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