CNS Core: Small: CNN-Accelerator Co-Design

CNS 核心：小型：CNN 加速器协同设计

• 批准号: 2216746
• 负责人: Niraj Jha
• 金额: $ 60万
• 依托单位: Princeton University
• 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: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2216746&HistoricalAwards=false
• 关键词: CNS; Core; Small; CNN; Accelerator

Recently, automated co-design of machine learning (ML) models and accelerator architectures has attracted significant attention from both the industry and academia. However, most co-design frameworks explore a limited search space. Furthermore, training the ML model and simulating the accelerator performance are computationally expensive. To address these limitations, this project proposes a convolutional neural network (CNN) and hardware-accelerator co-design framework. It will consist of two new benchmarking sub-frameworks, CNNBench and AccelBench, that will explore vastly expanded design spaces of CNNs and CNN accelerators. CNNBench will use a new search technique to converge to the optimal CNN architecture. AccelBench will perform cycle-accurate simulations for a diverse set of accelerator architectures in a vast design space. Values for a large number of hyperparameters need to be chosen while designing an accelerator for a given application. They include the number and size of processing elements and on-chip buffers, dataflow, main memory size and type, and those for many domain-specific modules like sparsity-aware computation and reduced-precision designs. Similarly, many hyperparameters come into play while designing a CNN as well: the number of layers, convolution type and size, normalization type, pooling type and size, structure of the final multi-layer perceptron head, activation function, training recipe, and many more. At the same time, a CNN architecture that has the highest performance may not meet resource constraints like energy consumption and memory footprint. The proposed co-design framework will efficiently navigate the joint CNN-accelerator design space for the target application under given user constraints. THe project expects at least an order of magnitude efficiency improvement in navigating this space relative to current baselines while delivering higher performance. Given the importance of the CNN-accelerator co-design problem to the industry, the proposed co-design framework will likely have a transformative impact on it. To effectively transfer research results, the project plans to continue our active industrial engagements. The project will recruit graduate/undergraduate female/minority students (the PI has supervised 15 female Ph.D. students so far as well as many female/minority undergraduate and high-school students). The project will ensure that the participants, including the broader public engaged through outreach, experience the interdisciplinary nature of the research. The research outcomes will be included in two undergraduate courses on Machine Learning and Embedded Computing. Outreach to high-school students is planned through Princeton Laboratory Learning Program. Broad dissemination to the academic and industrial communities will be achieved through published papers, posters, and seminars. In addition, various tools and models will be distributed online.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.

最近，机器学习（ML）模型和加速器体系结构的自动设计引起了行业和学术界的极大关注。 但是，大多数共同设计框架都探索了有限的搜索空间。此外，训练ML模型并模拟加速器性能在计算上很昂贵。为了解决这些限制，本项目提出了卷积神经网络（CNN）和硬件加速器共同设计框架。它将由两个新的基准子框架组成，即CNNbench和Accelbench，它们将探索CNN和CNN加速器的设计空间大大扩展。 CNNBENCH将使用一种新的搜索技术来收敛到最佳CNN体系结构。 Accelbench将对庞大的设计空间中的各种加速器体系结构进行周期准确的模拟。在设计给定应用程序的加速器时，需要选择大量超参数的值。它们包括处理元素和片上缓冲区的数量和大小，数据流，主存储器大小和类型，以及用于许多特定领域的模块（如稀疏感知计算和减少精确设计）的数量和大小。 同样，许多超参数也在设计CNN时发挥作用：层次，卷积类型和大小，归一化类型，合并类型和大小，最终多层Perceptron头的结构，激活功能，训练食谱等。 同时，具有最高性能的CNN体​​系结构可能无法满足能源消耗和内存足迹等资源限制。所提出的共同设计框架将有效地在给定的用户约束下为目标应用程序提供关节CNN-ACCELERATOR设计空间。 该项目预计至少在相对于当前基线的情况下导航该空间时至少提高了效率，同时提供了更高的性能。 鉴于CNN-Accelerator共同设计问题对行业的重要性，拟议的共同设计框架可能会对它产生变革性的影响。 为了有效地转移研究结果，该项目计划继续我们的活跃工业参与。 该项目将招募研究生/本科女性/少数族裔学生（到目前为止，PI已经监督了15名女性博士学位学生以及许多女性/少数民族本科生和高中生）。该项目将确保参与者，包括通过外展活动的更广泛的公众经历研究的跨学科性质。研究成果将包括在机器学习和嵌入式计算的两个本科课程中。通过普林斯顿实验室学习计划计划向高中生举报。 将通过发表的论文，海报和研讨会来实现对学术和工业社区的广泛传播。此外，将在线分发各种工具和模型。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估值得支持。