EAGER: III: Small: Green Granular Neural Networks with Fast FPGA-based Incremental Transfer Learning

目前，流行的机器学习系统运行在两种主要的处理单元上——中央处理单元（cpu）和图形处理单元（gpu）。这些单元产生大量的二氧化碳（CO2）排放，也浪费能源，因为传统的训练算法（例如，梯度下降算法和遗传算法）需要花费大量时间来优化数十亿个超参数，并且因为cpu和gpu不节能。由于估计的CO2排放量与总计算能力成正比，因此有效减少CO2排放的两种主要方法包括开发新型高速非传统训练算法以显着减少训练时间，以及使用新型低CO2排放的计算硬件。一项紧迫的挑战是开发一种新型的机器学习系统，该系统采用高速非传统训练算法，在绿色节能硬件上运行，以显著减少二氧化碳排放和能源消耗。基于快速现场可编程门阵列（FPGA）的绿色颗粒神经网络（GGNN）。基于fpga的增量迁移学习算法将比基于cpu - gpu的训练算法更有效地减少二氧化碳排放和能源消耗。由于FPGA是一种轻量化硬件，具有低CO2排放和低能耗的特点，因此采用FPGA快速求解线性方程组，直接计算浅层GGNN的超参数最优值。具有转移的颗粒知识的浅层GGNN可以增量式快速训练，为实时绿色计算应用做出有效的决策。将开发一种新的GGNN树来解决维度的诅咒。此外，浅层GGNN是可解释的，因为它可以生成可解释的粒度If-Then规则。本项目对人工智能、计算智能、数据挖掘、操作系统等相关课程的智能绿色计算教育也有帮助。本科生和研究生，包括代表性不足的学生将一起做研究工作。学生将受到良好的训练，成为未来的绿色机器学习劳动力，开发新的基于软件和硬件的机器学习算法，以大大减少二氧化碳排放和能源消耗。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Currently, popular machine learning systems running on the two main types of processing units -- central processing units (CPUs) and graphics processing units (GPUs). These units generate a lot of carbon dioxide (CO2) emissions and also waste energy because traditional training algorithms (e.g., gradient descent algorithms and genetic algorithms) take huge amount of time to optimize billions of hyperparameters, and because CPUs and GPUs are not energy efficient. Since the estimated CO2 emission amount is proportional to the total computational power, two major ways of effectively reducing CO2 emissions include developing novel high-speed non-traditional training algorithms to significantly reduce training times, and use novel computational hardware with low CO2 emissions. An urgent challenge is developing a novel machine learning system with high-speed non-traditional training algorithms running on green and energy efficient hardware to significantly reduce both CO2 emissions and energy consumption.A novel shallow software-hardware-based green granular neural network (GGNN) with new fast, field-programmable gate array (FPGA). FPGA-based incremental transfer learning algorithms will be developed to reduce both CO2 emissions and energy consumption more effectively than the CPU-GPU-based training algorithms. Since FPGA is a light-weight hardware with low CO2 emissions and low energy consumption, the FPGA is used to quickly solve a system of linear equations to directly calculate optimal values of hyperparameters of the shallow GGNN. The shallow GGNN with transferred granular knowledge can be trained incrementally and quickly to make efficient decisions for real-time green computing applications. A new GGNN tree will be developed to resolve the curse of dimensionality. In addition, the shallow GGNN is explainable because it can generate interpretable granular If-Then rules. This project is also useful for improving intelligent green computing education for relevant courses such as artificial intelligence, computational intelligence, data mining, and operating systems. Undergraduate students and graduate students including underrepresented students will do research works together. Students will be well trained to become future green machine learning workforce to develop novel software-hardware-based machine learning algorithms to greatly reduce both CO2 emissions and energy consumption.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.