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 | Conference PDF | Archive PDF | Plain Text | 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 {hy34, yz87, cl114, yingyan.lin}@rice.edu, {xiaohan.chen, atlaswang}@utexas.edu {sicheng.li, zihao.liu}@alibaba-inc.com |
| 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]. |