Enabling Cross-Domain Communication: How to Bridge the Gap between AI and HW Engineers

TL;DR

This paper discusses establishing a comprehensive methodology for designing complex AI hardware-software systems, emphasizing the importance of languages and tools to facilitate communication among diverse engineering teams.

Contribution

It highlights the need for an end-to-end co-design methodology for AI systems involving multiple processing units and explores how languages and tools can bridge communication gaps.

Findings

Identifies the lack of unified design methodology as a key challenge.

Proposes the role of languages and tools in enabling system-level co-design.

Emphasizes the importance of understanding hardware, workload mapping, and application domain.

Abstract

A key issue in system design is the lack of communication between hardware, software and domain expert. Recent research work shows progress in automatic HW/SW co-design flows of neural accelerators that seems to make this kind of communication obsolete. Most real-world systems, however, are a composition of multiple processing units, communication networks and memories. A HW/SW co-design process of (reconfigurable) neural accelerators, therefore, is an important sub-problem towards a common co-design methodology. The ultimate challenge is to define the constraints for the design space exploration on system level - a task which requires deep knowledge and understanding of hardware architectures, mapping of workloads onto hardware and the application domain, e.g. artificial intelligence. For most projects, these skills are distributed among several people or even different teams which is one of the major reasons why there is no established end-to-end development methodology for digital systems. This position paper discusses possibilities how to establish such a methodology for systems that include (reconfigurable) dedicated accelerators and outlines the central role that languages and tools play in the process.