SBIR Phase I: A Mixed-Computation Neural Network Acceleration Stack for Edge Inference

SBIR 第一阶段：用于边缘推理的混合计算神经网络加速堆栈

• 批准号: 2304304
• 负责人: Mahdi Nazemi
• 金额: $ 27.49万
• 依托单位: NOUVAI INC.
• 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: 2023-12-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304304&HistoricalAwards=false
• 关键词: SBIR; Phase; Mixed; Computation; Neural

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to improve the sustainability of artificial intelligence by reducing carbon emissions for training neural networks and performing inference at the edge. Additionally, the technology will spawn new applications and use cases for edge inference (including personal health, advanced data analytics, and informed decision-making), resulting in significant improvements in people's lives and well-being. The commercial potential is substantial (i.e., tens of billions of dollars annually), as are the potential economic benefits to US high-technology industries.This Small Business Innovation Research (SBIR) Phase I project sets out to develop a mixed-computation neural network acceleration stack utilizing optimally designed and provisioned hardware resources. This acceleration stack empowers a heterogeneous hardware realization of a neural network inference engine whereby computations required in various network layers may be done by using different number systems and different precision levels. The acceleration stack can thus achieve very high inference speed and energy efficiency while maintaining the inference accuracy compared to a homogeneous hardware realization of the network using 16-bit floating point computations. To support the design, optimization, and runtime efficiency of this edge inference accelerator, a full suite of software and design automation tools comprising a distiller for neural network architecture optimization and training, a logic synthesizer for generating optimized gate-level realization of very large and complex Boolean and multi-valued logic functions, a compiler for generating and scheduling control-flow and data path instructions that are executed on the target fabric, and a runtime system for orchestrating data movement will also be provided. The resulting edge inference accelerator will be deployable on resource-constrained, energy-limited, and cost-sensitive edge 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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛/商业影响是通过减少培训神经网络的碳排放并在边缘进行推理来提高人工智能的可持续性。此外，该技术将为边缘推理（包括个人健康，高级数据分析和知情决策）产生新的应用程序和用例，从而大大改善人们的生活和福祉。商业潜力是巨大的（即每年数十亿美元），对美国高技术行业的潜在经济利益也是如此。这项小型企业创新研究（SBIR）I阶段项目旨在开发使用最佳设计和提供的设计和提供的硬件资源来开发混合构成神经网络加速堆栈。这种加速堆栈赋予神经网络推理引擎的异质硬件实现，从而可以通过使用不同的数字系统和不同的精度级别来完成各种网络层中所需的计算。因此，与使用16位浮点计算对网络的均匀硬件实现相比，加速堆栈可以达到非常高的推理速度和能量效率，同时保持推理精度。 To support the design, optimization, and runtime efficiency of this edge inference accelerator, a full suite of software and design automation tools comprising a distiller for neural network architecture optimization and training, a logic synthesizer for generating optimized gate-level realization of very large and complex Boolean and multi-valued logic functions, a compiler for generating and scheduling control-flow and data path instructions that are executed on the target还将提供织物和用于编排数据移动的运行时系统。由此产生的边缘推理加速器将可以在资源约束，能源限制和成本敏感的边缘设备上进行部署。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来获得支持的。