Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware
Authors: Xiandong Zhao, Ying Wang, Xuyi Cai, Cheng Liu, Lei Zhang
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, three sets of experiments on Cifar10, Image Net and Pascal VOC datasets are presented. |
| Researcher Affiliation | Academia | Institute of Computing Technology, Chinese Academy of Sciences1 University of Chinese Academy of Sciences2 State Key Laboratory of Computer Architecture3 {zhaoxiandong,wangying2009,caixuyi18s,liucheng,zlei}@ict.ac.cn |
| Pseudocode | Yes | Algorithm 1 LLSQ |
| Open Source Code | No | The paper mentions using "Py Torch Model Zoo" and "open-source Py Torch implementation of YOLOv2" as baselines, but does not explicitly state that their own LLSQ code is open-source or provide a link to it. |
| Open Datasets | Yes | In this section, three sets of experiments on Cifar10, Image Net and Pascal VOC datasets are presented. |
| Dataset Splits | No | The paper mentions "val accuracy" in Figure 5, implying a validation set was used, but it does not provide specific details on the dataset splits (e.g., percentages, sample counts, or a detailed splitting methodology) for reproducibility. |
| Hardware Specification | Yes | Finally, we deploy the quantized network onto our specialized integer-only neural network accelerator... Our accelerators adopt the typical 2D systolic array architecture... The MAC unit in each PE consists of a 4-bit multiplier and a 16-bit accumulator... Finally, we implement the 8/4/2-bit integer neural network processors with Synopsys Design Compiler (DC) under the 40nm technology, clocked at 800MHz. |
| Software Dependencies | No | The paper mentions "Py Torch (Paszke et al., 2017)" for implementation and "Synopsys Design Compiler (DC)" for hardware implementation, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | we train the reference network for 400 epochs using an initial learning rate of 2e-2. And for the training of the quantized network, we use a warmup learning rate scheduler in the first ten epochs with an initial learning rate of 2e-3. In all quantization experiments, the total training epochs are 100. |