SHF: Small: Methods and Architectures for Optimization and Hardware Acceleration of Spiking Neural Networks

SHF：小型：尖峰神经网络优化和硬件加速的方法和架构

• 批准号: 2310170
• 负责人: Peng Li
• 金额: $ 59.93万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310170&HistoricalAwards=false
• 关键词: SHF; Small; Methods; Architectures; Optimization

Artificial intelligence is a powerful cross-cutting technology and is expected to promote broad advancements in science and technology as well as foster social benefits. To this end, exploring novel computational principles inspired by the brain may offer promising new avenues to enable artificial intelligence. This project is positioned to address key challenges in designing and engineering brain-inspired spiking neural models. As such, it may lead to methods, tools, and hardware system designs that will ultimately support new generations of software- and hardware-based artificial intelligence systems with potentially significantly improved performance and efficiency. This project will produce educational materials to be integrated into undergraduate- and graduate-level curricula on artificial intelligence and hardware system design, thereby providing workforce training opportunities in these areas of importance. The principal investigator will actively recruit undergraduate, underrepresented, and female students for research participation and training while partnering with various outreach programs. The results of this award may be derived in a variety of forms, including algorithms, software design tools, and hardware architectures and implementations that will be disseminated in broad research and industrial communities through publications, workshops, talks, and research collaborations. Engagement with US high-tech industries and other research organizations will be sought to broaden the impact of this work, promote potential technology transfer into practice, and offer additional mentoring and training of students under diverse industrial and research settings.Deep learning based on conventional non-spiking artificial neural networks (ANNs) has achieved great success in many application domains in recent years. Nevertheless, the conventional ANNs cannot immediately explore temporal codes and lack energy-efficient event-based processing. On the other hand, it is believed that attaining near-human-level intelligence requires computing paradigms that better mimic biological brains. As such, spiking neural networks (SNNs) offer a complementary biologically-plausible approach to facilitating future artificial intelligence systems. However, there are key roadblocks to a wider adoption of spiking neural networks. SNNs are much harder to train than conventional ANNs. There is a general lack of insights and systemic approaches for designing computationally-powerful SNNs, particularly SNNs with recurrent connections. Hardware acceleration of SNNs is hampered by complex data dependencies across both time and space, and unstructured firing sparsity. This work will start out by developing much needed accurate SNN training methods that can robustly learn precise temporal behavior and jointly tune spike count and spike timing. Scalable architectural design of recurrent SNNs and novel automated spiking neural structural optimization methods will be developed to support the design of computationally powerful SNNs. To enable energy-efficient high-throughput hardware acceleration, dedicated SNN hardware accelerator architectures that minimize expensive data movements and facilitate parallel processing in both space and time will be designed. Application-independent spike coding, spike compression, and architectures exploring unstructured firing sparsity will be investigated for SNN hardware acceleration. High-performance SNN hardware accelerators will be demonstrated on field-programmable gate-array devices.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.

人工智能是一项强大的跨切割技术，有望促进科学和技术方面的广泛进步，并促进社会利益。为此，探索受大脑启发的新型计算原理可能会提供有希望的新途径来实现人工智能。该项目的定位旨在应对设计和工程脑启发的尖峰神经模型的关键挑战。因此，它可能会导致方法，工具和硬件系统设计，最终将支持新一代的基于软件和硬件的人工智能系统，具有显着提高性能和效率。该项目将生产教育材料，以融入人工智能和硬件系统设计的本科和研究生级课程，从而在这些重要性领域提供劳动力培训机会。首席调查员将积极招募本科，代表性不足和女学生进行研究参与和培训，同时与各种外展计划合作。该奖项的结果可以以各种形式得出，包括算法，软件设计工具以及硬件体系结构和实施，这些算法将通过出版物，研讨会，演讲和研究合作在广泛的研究和工业社区中传播。将寻求与美国高科技行业和其他研究组织的参与，以扩大这项工作的影响，将潜在的技术转移促进实践，并为在多样化的工业和研究环境下提供对学生的额外指导和培训。近年来，基于传统的非加价人工神经网络（ANN），深入学习在近年来在许多应用领域取得了巨大的成功。然而，传统的ANN无法立即探索时间代码，并且缺乏基于节能的事件处理。另一方面，据信，达到近乎人类的智能需要更好地模拟生物学大脑的计算范式。因此，尖峰神经网络（SNNS）提供了一种互补的生物学知识方法来促进未来的人工智能系统。但是，更广泛地采用尖峰神经网络有关键的障碍。 SNN比传统的ANN更难训练。通常缺乏用于设计计算功能的SNN的见解和系统方法，尤其是具有经常性连接的SNN。 SNN的硬件加速度受到时间和空间的复杂数据依赖性以及非结构化的触发性的阻碍。这项工作将开始开发急需的准确的SNN训练方法，这些方法可以鲁and学习精确的时间行为，并共同调整尖峰计数和尖峰计时。将开发复发性SNN和新型自动尖峰神经结构优化方法的可扩展体系结构设计，以支持计算功能强大的SNN的设计。为了启用节能高通量硬件加速度，专用的SNN硬件加速器体系结构将设计最小化昂贵的数据移动并促进时空和时间的并行处理。将研究与应用程序无关的尖峰编码，尖峰压缩和架构探索非结构化的射击稀疏性，以进行SNN硬件加速度。高性能SNN硬件加速器将在现场可编程的栅极阵列设备上进行证明。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估。