Genetic Algorithm-Based Energy-Aware CNN Quantization for Processing-In-Memory Architecture
| Autor: | Dae Hyun Kim, Shimeng Yu, Beomseok Kang, Anni Lu, Yun Long, Saibal Mukhopadhyay |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 11:649-662 |
| ISSN: | 2156-3365 2156-3357 |
| Popis: | We present a genetic algorithm based energy-aware convolutional neural network (CNN) quantization framework (EGQ) for processing-in-memory (PIM) architectures. EGQ predicts layer-wise dynamic energy consumption based on the number of ADC access. Also, EGQ automatically optimizes layer-wise weight/activation bitwidth that can reduce total dynamic energy with negligible accuracy loss. As EGQ requires basic CNN model information such as weight/activation dimensions to predict the dynamic energy, various models can be compressed by EGQ. We analyse the effectiveness of EGQ on the area, dynamic energy, and energy efficiency of PIM architectures for VGG-19, ResNet-18, and ResNet-50 using NeuroSim. We observe EGQ is an effective approach for the CNN models to reduce the dynamic energy in various PIM designs with SRAM, RRAM, and FeFET technologies. EGQ achieves 6.1 bit of average weight bitwidth and 6.3 bit of average activation bitwidth in ResNet-18, that improves energy efficiency by 6.5× than the 16-bit model. For ResNet-18 with CIFAR-10, 2.5 bit and 3.9 bit of average weight and activation bitwidth are achieved. Both results show the negligible accuracy loss of 2%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|