POSE: Phase II: Building an Open-Source Ecosystem for Deep-Learning Hardware-Software Co-Design

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Domain-specific acceleration is one of the most promising approaches to further improve performance and energy efficiency applications like deep learning. Despite the large number of startups and large companies developing specialized hardware and software for deep learning, all the existing implementations are proprietary, without a viable, freely open-source deep-learning hardware-software stack. The lack of an open and shared ecosystem not only makes it extremely hard to compare different implementations, it also significantly slows innovation and increases design costs as every organization needs to start their implementations from scratch. The overall objective of this proposal is to establish an open-source ecosystem that enables the development of full-stack deep-learning systems at scale to build next-generation deep-learning platforms. If successful, the outcomes of this project point toward a future in which developers with a great idea in either deep-learning hardware or software can quickly evaluate, design, and demonstrate their idea in an end-to-end fashion on real hardware and software, significantly lowering the design cost and accelerating the pace of innovation. Specifically, the open-source product consists of three key components based on our mature research projects: Exo for code generation (https://github.com/exo-lang/exo), Gemmini for deep-learning accelerator design (https://github.com/ucb-bar/gemmini), and Chipyard for system-on-chip integration (https://github.com/ucb-bar/chipyard). In particular, the proposed hardware-software ecosystem will fundamentally address challenges in 1) how to evaluate the end-to-end performance of deep-learning accelerators with the software stack; 2) how to generate efficient deep-learning hardware accelerators for specific scenarios; and 3) how to integrate accelerators with general-purpose cores and evaluate them in FPGA and fabrication. The hardware-software ecosystem established from this project will create a diverse and highly motivated open-source community for future developments on deep-learning acceleration through hardware-software co-design. 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.