Optimization of Mixed-precision Neural Architecture with Knowledge Distillation

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 이혁재.Quantization은 메모리와 계산 능력이 제한된 edge device에서 deep neural network를 수행하기 위해 필수적인 프로세스이다. 그러나 bit-width 및 transformation function을 포함하여 똑같은 quantization 을 모든 레이어에 그대로 적용하는 uniform-precision quantization은 심각한 성능 저하를 겪는 것으로 널리 알려져 있다. 한편 각각의 레이어에 서로 다른 quantization 을 찾아서 적용하는 것은 후보군의 수가 레이어 수에 따라 기하급수적으로 증가하기 때문에 적용하기 어렵다. 이 문제를 해결하기 위해, 본 연구에서는 Knowledge Distillation 기법을 활용하여 선형시간 내에 검색 공간을 효율적으로 탐색하는 방법을 제안한다. 특히, 제안된 방법은 대상 레이어에 대한 quantization 의 영향을 추정하기 위해 레이어별로 loss function을 공식화하였다. 레이어간에 의존성이 최소라는 가정에 근거하여 각 레이어별 적용을 개별적으로 결정해서 성능의 손실을 최소화한다. 하드웨어 친화적인 quantization 만 사용하여 image classification과 object detection 에 대한 실험을 실시하였다. 그 결과, CIFAR-10 데이터 세트에서 크기가 13.65배 작은 가장 효율적인 mixed-precision의 ResNet20 모델로 93.62%의 정확도를 달성하였으며, 이는 기본 full-precision 모델보다 0.19% 낮은 수준이다. VOC 데이터 세트에서, 제안된 방법은 평균 precision 이 63.87% 인 mixed-precision Sim-YOLOv2-FPGA 모델을 생성하였고, 동일 압축률의 모든 uniform-precision 모델보다 성능이 뛰어나다. 제안하는 방법은 다른 최첨단 mixed-precision quantization 접근법과 유사한 효율을 달성하면서도 실행은 단순하다.Quantization is an essential process in the deployment of deep neural networks on edge devices which only have limited memory and computation capacity. However, it is widely known that the straightforward uniform-precision quantization method, which applies the same quantization scheme including bit-width and transformation function to all layers, suffers a severe performance degradation. Meanwhile, finding and assigning different quantization schemes to different layers is challenging as the number of candidates is exponential to the the number of layers. To address this problem, this study proposes a method that utilizes Knowledge Distillation technique to efficiently explore the search space in linear time. In particular, the proposed method formulates a per-layer loss function to estimate the impact of a quantization scheme on a target layer. Based on the assumption that the dependence among layers is minimal, the assignment is then decided for each layer separately to minimize the performance loss. Experiments are conducted for both image classification and object detection task, using only hardware-friendly quantization schemes. The results show that the most efficient mixed-precision ResNet20 model with 13.65 times smaller size can still achieve up to 93.62% accuracy on CIFAR-10 dataset, which is only 0.19% lower than the baseline full-precision model. On VOC dataset, the proposed method generates a mixed-precision Sim-YOLOv2-FPGA model with a mean average precision of 63.87, which outperforms all uniform-precision models with the same compression rate. The proposed method is practically simple to carry out while still achieving a comparable efficiency to other state-of-the-art approaches on mixed-precision quantization.Chapter 1: Introduction 1 Chapter 2: Related Work 4 2.1. Quantization techniques 4 2.2. Uniform quantization 5 2.3. Shifter quantization 6 2.4. Knowledge Distillation 8 2.5. Neural Architecture Search 10 Chapter 3: Knowledge-Distillation Mixed-Precision 11 3.1. Complexity challenge and solution 11 3.2. Impact assessment via Knowledge Distillation 15 3.3. Parallel Knowledge Distillation training 17 3.4. Candidate architecture generation 18 3.5. Final model fine-tuning 20 3.6. Summary 21 Chapter 4: Experimental Results 23 4.1. Final model fine-tuning time reduction 23 4.2. Scalable and flexible training 25 4.3. Effectiveness of Knowledge Distillation Mixed Precision 27 4.4. Intra-layer mixed precision 30 Chapter 5: Conclusion and Future Work 32 Appendix 33 A. Experiment with Sim-YOLOv2-FPGA on VOC dataset 33 B. Experiment with ResNet20 and ResNet32 on CIFAR-10 34 Reference 35 초 록 38 Acknowledgement 40Maste

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