Robust I/O-compute concurrency for machine learning pipelines in constrained cyber-physical devices - Poroor, Jayaraj; Lal, Akash; Ghanta, Sandesh (LCTES, 2021)
Cyberphysical systems have numerous industrial and commercial applications. Such systems are often built using low-resource devices that gather and process data, using machine-learning (ML) models, to make intelligent decisions and provide value to users. Programming such low-resource devices with an impoverished system runtime is often challenging. This paper presents a new domain-specific language called PiCon for programming ML pipelines in low-resource devices. PiCon allows safe I/O-compute concurrency, ruling out a large class of errors, while providing a simple and sequential coding abstraction to the programmer. PiCon compiles to C code and easily interfaces with existing C/C++ code. Furthermore, the generated code does not rely on multi-threading support or dynamic memory allocation, dramatically reducing its footprint on the device. We present experience porting two real-world ML applications that demonstrate simplification in programmability, in addition to several safe-by-construction guarantees