Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
ShiftAddNet: A Hardware-Inspired Deep Network
Authors: Haoran You, Xiaohan Chen, Yongan Zhang, Chaojian Li, Sicheng Li, Zihao Liu, Zhangyang Wang, Yingyan Lin
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments and ablation studies, all backed up by our FPGA-based Shift Add Net implementation and energy measurements. |
| Researcher Affiliation | Collaboration | Department of Electrical and Computer Engineering, Rice University Department of Electrical and Computer Engineering, The University of Texas at Austin Alibaba DAMO Academy EMAIL, EMAIL EMAIL |
| Pseudocode | No | The paper includes mathematical formulations for backpropagation (e.g., equations 2, 3, 4, 5, 6) but does not present them in a pseudocode or algorithm block format. |
| Open Source Code | Yes | Codes and pre-trained models are available at https://github.com/RICE-EIC/Shift Add Net. |
| Open Datasets | Yes | Models and datasets. We consider two DNN models (i.e., Res Net-20 [35] and VGG19-small models [36]) on six datasets: two classification datasets (i.e., CIFAR-10/100) and four Io T datasets (including MHEALTH [37], Flat Cam Face [38], USCHAD [39], and Head-pose detection [40]). |
| Dataset Splits | No | The paper specifies training and testing splits for some datasets (e.g., '80% for training and the remaining 20% for testing' for Head-pose), but it does not explicitly mention a separate validation split. |
| Hardware Specification | Yes | Specifically, we implement Shift Add Net on a ZYNQ-7 ZC706 FPGA board [9] and collect all real energy measurements for benchmarking. ... FPGA (ZYNQ-7 ZC706) |
| Software Dependencies | No | The paper mentions using an 'SGD solver' but does not provide specific version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | Training settings. For the CIFAR-10/100 and Head-pose datasets, the training takes a total of 160 epochs with a batch size of 256, where the initial learning rate is set to 0.1 and then divided by 10 at the 80-th and 120-th epochs, respectively, and a SGD solver is adopted with a momentum of 0.9 and a weight decay of 10 4 following [42]. |