Enabling Cross-Domain Communication: How to Bridge the Gap between AI and HW Engineers
It tackles the communication barrier between AI and hardware engineers in system design, but is incremental as it builds on existing co-design flows.
This position paper addresses the lack of an established end-to-end development methodology for digital systems that include reconfigurable neural accelerators, due to distributed expertise across hardware, software, and domain teams, and outlines the role of languages and tools in bridging this gap.
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.