Genetic Algorithm-Based Energy-Aware CNN Quantization for Processing-In-Memory Architecture
Beomseok Kang, Anni Lu, Yun Long, Daehyun Kim, Shimeng Yu, Saibal Mukhopadhyay
Abstract
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.5\times $ </tex-math></inline-formula> 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%.