Abstract

In today’s digital landscape, the exponential growth of data has become the driving force behind cutting-edge applications, such as genome analysis and machine learning applications, revolutionizing our approach to healthcare and overall living quality. However, this unprecedented deluge of data poses a formidable challenge to traditional von Neumann computer architectures. The inefficiencies arising from the constant data movement between processors and memory consume a substantial portion of both execution time and energy when running modern applications on conventional von Neumann computers. To reduce this significant data movement, data-centric architectures, particularly processing-in-memory accelerators, emerge as a promising solution by enabling the processing of data directly where it resides. Nonetheless, most existing data-centric architectures primarily focus on accelerating specific arithmetic operations, inadvertently leaving a substantial gap between the architectural enhancements and the holistic needs of modern applications. Concurrently, conventional software optimizations often treat the architecture as a black box, which inherently limits the potential acceleration of applications.

This talk seeks to bridge the gaps between modern applications and data-centric architectures and revolutionize the landscape of data-centric acceleration for two vital categories of modern applications: genome analysis and machine learning. Firstly, this talk offers a comprehensive analysis of the pressing challenges within state-of-the-art genome analysis pipelines and introduces an innovative end-to-end data-centric acceleration approach achieved through seamless software-and-hardware co-design. Secondly, this talk illuminates the path to closing the gap between data-centric accelerators and the execution of real-world applications by presenting a compelling case study centered on a crucial machine learning application based on generative adversarial networks (GANs). Furthermore, this talk delves into the intricate challenges of data-centric acceleration for modern applications and explores potential solutions to surmount these obstacles, paving the way for a future where data-centric acceleration seamlessly integrates with the ever-evolving landscape of advanced applications.