Awesome

Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators

Yonggan Fu, Yongan Zhang, Yang Zhang, David Cox, Yingyan Lin

Accepted at ICML 2021 [Paper Link].

Overview

• While maximizing deep neural networks’ (DNNs’) acceleration efficiency requires a joint search/design of three different yet highly coupled aspects, including the networks, bitwidths, and accelerators, the challenges associated with such a joint search have not yet been fully understood and addressed. To tackle these daunting challenges towards optimal and fast development of DNN accelerators, we propose a framework dubbed Auto-NBA to enable jointly searching for the Networks, Bitwidths, and Accelerators, by efficiently localizing the optimal design within the huge joint design space for each target dataset and acceleration specification.

Method

Problem Formulation

• Our Auto-NBA targets a scalable yet generic joint search framework, which we formulate as a bi-level optimization problem:

Heterogeneous Sampling for Bit-wise Search

• To tackle the dilemma between memory explosion or biased search of bit-wise search, we propose a simple yet effective heterogeneous sampling strategy:

Differentiable Accelerator Search Engine

• We propose a differentiable search engine to efficiently search for the optimal accelerator (including the micro-architectures and mapping methods) given a DNN and its precision:

Overall Joint Search Framework

• We propose the single-path forward / multi-path backward strategy during joint search and acquire the layer/block-wise hardware cost using the single-path network derived from the current forward as a proxy of the finally derived network, which tackles (1) the prohibitively large joint space, (2) the entangled co-adaptation and cooperation issues among different network and precision choices, and (3) the chicken-and-egg problem associated with network-accelerator co-search.

Results

• The searched networks by our Auto-NBA consistently push forward the frontier of accuracy-FPS trade-offs, compared to all SOTA baselines. For example, compared with the most relevant baseline EDD, we achieve a +1.3% higher accuracy together with a 1.59× better FPS.

Code Usage

• A two-stage process is needed to generate the matched network/bitwidth/accelerator solutions, i.e., the search and training stage. The corresponding settings are specified in config_search.py and config_train.py, respectively. In particular, plz specify the search settings in config_search.py and search the optimal network/bitwidth/accelerator solutions through train_search.py. Next, specify training settings in config_train.py and train the searched network/bitwidth from scratch through train.py (The best searched arch will be saved at ckpt/search/arch.pt which is the default path for train.py to read.)

Prerequisites

• See env.yml for the complete conda environment. Create a new conda environment:

conda env create -f env.yml
conda activate pytorch

The Search Stage

1. Specify the search setting in config_search.py:

C.dataset_path = "path-to-dataset"
C.latency_weight = 1e-10

2. Run train_search.py:

python train_search.py

The Training Stage

1. Specify the training setting in config_train.py:

C.dataset_path = "path-to-dataset"
C.load_path = "path-to-searched-arch"

2. Run train.py on each of your nodes:

python train.py

Citation

@article{fu2021auto,
  title={Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators},
  author={Fu, Yonggan and Zhang, Yongan and Zhang, Yang and Cox, David and Lin, Yingyan},
  journal={arXiv preprint arXiv:2106.06575},
  year={2021}
}