Efficient Distributed Inference of Deep Neural Networks via Restructuring and Pruning
Authors: Afshin Abdi, Saeed Rashidi, Faramarz Fekri, Tushar Krishna
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the performance of the Re Purpose framework, we consider different DNN architectures and compare the accuracy, communication, and wall-clock times of the proposed framework to the following approaches; (1) naive implementation where the input data (or locally computed features) are communicated to all nodes in the network, so they all have the entire input data and process the entire deep model locally. [...] The results are shown in Fig. 7 for P = 6 sensors. [...] Figures 7a and 8a compare the performance of Re Purpose with the baseline, naive sparsification, and model distillation. |
| Researcher Affiliation | Academia | School of Electrical and Computer Engineering, Georgia Institute of Technology |
| Pseudocode | Yes | Algorithm 1: Re Purpose algorithm for a single layer |
| Open Source Code | No | The paper mentions ASTRA-sim is 'an open-source distributed Deep Learning platform simulator' but does not provide a statement or link for the open-sourcing of their own proposed methodology (Re Purpose) code. |
| Open Datasets | Yes | Next, we consider a network of P sensors where each sensor observes an image of a digit xi (from MNIST dataset) |
| Dataset Splits | No | The paper does not explicitly provide specific percentages or counts for training, validation, and test dataset splits. It mentions 'fine-tuning' or 'post-training' but without specifying how the data was partitioned for these stages. |
| Hardware Specification | Yes | Compute Memory Bandwidth Datacenter 125 TOPS 32GB 150 GB/s (NVLink) Edge 0.5 TOPS 1GB 100 MB/s (Ethernet) |
| Software Dependencies | No | The paper mentions 'ASTRA-sim' and 'NVIDIA NCCL' but does not provide specific version numbers for these software components or any other libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | We applied Re Purpose with η1 = 0, η2 {0.01, 0.1} (figures 4(c)-(d)). |