BatchQuant: Quantized-for-all Architecture Search with Robust QuantizerDownload PDF

21 May 2021, 20:47 (modified: 25 Jan 2022, 16:32)NeurIPS 2021 PosterReaders: Everyone
Keywords: Joint Neural Architecture Search and Quantization, Neural Architecture Search, Mixed-Precision Quantization, Model Compression
TL;DR: We propose a novel quantizer to stabilize single-shot supernet training for joint mixed-precision quantization and architecture search. Our approach discovers quantized architectures with SOTA efficiency within fewer GPU hours than previous methods.
Abstract: As the applications of deep learning models on edge devices increase at an accelerating pace, fast adaptation to various scenarios with varying resource constraints has become a crucial aspect of model deployment. As a result, model optimization strategies with adaptive configuration are becoming increasingly popular. While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space. Existing methods tend to either limit the architecture search space to a small set of options or limit the quantization policy search space to fixed precision policies. To this end, we propose BatchQuant, a robust quantizer formulation that allows fast and stable training of a compact, single-shot, mixed-precision, weight-sharing supernet. We employ BatchQuant to train a compact supernet (offering over $10^{76}$ quantized subnets) within substantially fewer GPU hours than previous methods. Our approach, Quantized-for-all (QFA), is the first to seamlessly extend one-shot weight-sharing NAS supernet to support subnets with arbitrary ultra-low bitwidth mixed-precision quantization policies without retraining. QFA opens up new possibilities in joint hardware-aware neural architecture search and quantization. We demonstrate the effectiveness of our method on ImageNet and achieve SOTA Top-1 accuracy under a low complexity constraint (<20 MFLOPs).
Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
Code: https://github.com/bhpfelix/QFA
13 Replies

Loading