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

EAGER：III：小型：具有基于 FPGA 的快速增量迁移学习的绿色粒度神经网络

• 批准号: 2234227
• 负责人: Yanqing Zhang
• 金额: $ 25.52万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234227&HistoricalAwards=false
• 关键词: EAGER; III; Small; Green; Granular

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.

目前，流行的机器学习系统运行在两种主要类型的处理单元上--中央处理单元(CPU)和图形处理单元(GPU)。这些单元会产生大量二氧化碳(CO2)排放，还会浪费能源，因为传统的训练算法(例如，梯度下降算法和遗传算法)要花费大量时间来优化数十亿个超参数，而且CPU和GPU的能效不高。由于估计的二氧化碳排放量与总计算能力成正比，有效减少二氧化碳排放的两个主要方法包括开发新的高速非传统训练算法以显著减少训练时间，以及使用低二氧化碳排放的新计算硬件。一个迫切的挑战是开发一种新型的机器学习系统，该系统采用运行在绿色节能硬件上的高速非传统训练算法，以显著减少二氧化碳排放和能源消耗。基于现场可编程门阵列的增量转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耗。由于现场可编程门阵列是一种轻量化、低二氧化碳排放、低能耗的硬件，因此利用现场可编程门阵列快速求解线性方程组，直接计算出浅层GGNN超参数的最优值。具有传递粒度知识的浅层GGNN可以增量地、快速地训练，为实时绿色计算应用做出有效的决策。将开发一种新的GGNN树来解决维度灾难。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。该项目还有助于提高人工智能、计算智能、数据挖掘和操作系统等相关课程的智能绿色计算教育。本科生和研究生，包括代表性不足的学生，将一起进行研究工作。学生将接受良好的培训，成为未来的绿色机器学习劳动力，开发基于软件和硬件的新型机器学习算法，以大幅减少二氧化碳排放和能源消耗。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Green Granular Neural Network with Efficient Software-FPGA Co-designed Learning

具有高效软件-FPGA 协同设计学习的绿色粒度神经网络

• 发表时间: 2023
• 期刊: IEEE 22nd International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC'2023
• 作者: Chu, Huaiyuan;Zhang, Yanqing
• 通讯作者: Zhang, Yanqing