SHF: Small: Acceleration Strategies for Emerging Life Science Workloads

计算方法引发了医疗保健领域的几项进步。精确医学的前景取决于以非常高的性能执行大量计算的能力。许多研究工作探索了为基因组分析量身定做的硬件/软件创新。然而，用于基因组分析的新型定制硬件(又名加速器)即使有商业上的成功，也没有多少成功。这种有限的商业成功的一个关键因素是以相对较低的产量生产的大型定制芯片的高成本。另一方面，人工智能(AI)的流行和成功导致了AI芯片的更大市场，以及一系列可用于AI的商业芯片。这个项目回答的关键研究问题是：能否利用人工智能和人工智能硬件的成功来提高小市场领域的性能和效率？这个项目试图回答医疗保健领域的这个问题，重点关注两个新兴的工作负载-计算病理学和基因组分析。它还将为社区开发软件基础设施，充实教育课程，并帮助发展一支多样化的、训练有素的劳动力队伍。该项目将采用以下新方法：在现有硬件加速器上为新兴领域提供自举加速器，为人工智能提供加速器。特别是，它探索了AI+X方法，其中硬件设计为处理AI工作负载和另一个小型市场工作负载X(在本项目中，X将是计算病理学或基因组分析)。该项目立志于设计可以为人工智能和医疗保健领域大规模生产的加速器芯片，同时几乎达到专门用于人工智能或医疗保健领域的加速器的效率。该项目将设计一个可配置的体系结构，提供高灵活性、高效率和低成本。它将探索算法重组，以充分利用AI+X硬件的能力。这包括缓解内存瓶颈、确定软件到硬件的最佳映射以及将计算内核转换为易于加速的张量运算的技术。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。

Computational approaches have sparked several advancements in healthcare. The promise of precision medicine hinges on the ability to perform a vast amount of computations at very high performance. Many research efforts have explored hardware/software innovations customized for genomic analysis. However, novel custom hardware (aka accelerators) for genomic analysis has not had much if any commercial success. A key factor in this limited commercial success is the high cost of a large custom chip that is produced at relatively low volume. On the other hand, the popularity and success of artificial intelligence (AI) has resulted in a much larger market for AI chips, and a range of commercially available chips for AI. The key research question being answered in this project is: can the success of AI and AI hardware be leveraged to advance performance and efficiency for small-market domains? This project attempts to answer this question for the healthcare domain with a focus on two emerging workloads - Computational Pathology and Genomic Analysis. It will also develop software infrastructure for the community, augment educational curricula, and help grow a diverse, well-trained workforce.The project will pursue the following novel approach: bootstrap accelerators for emerging domains on existing hardware accelerators for AI. In particular, it explores an AI+X approach, where the hardware is designed to handle AI workloads and another small-market workload X (in this project, X will be either Computational Pathology or Genomic Analysis). The project aspires to design accelerator chips that can be produced at high volume for both AI and healthcare domains, while nearly matching the efficiency of accelerators dedicated exclusively for AI or exclusively for healthcare. The project will design a configurable architecture that provides high flexibility, high efficiency, and low cost. It will explore algorithm re-structuring to fully exploit the capabilities of AI+X hardware. This includes techniques to alleviate the memory bottleneck, identify optimal mapping of software to hardware, and transform compute kernels into easily-accelerated tensor operations.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.