Enumerating Hardware-Software Splits with Program Rewriting
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A core problem in hardware-software codesign is in the sheer size of the design space. Without a set ISA to constrain the hardware-software interface, the design space explodes. This work presents a strategy for managing the massive hardware-software design space within the domain of machine learning inference workloads and accelerators. We first propose EngineIR, a new language for representing machine learning hardware and software in a single program. Then, using equality graphs – a data structure from the compilers literature – we suggest a method for efficiently enumerating the design space by performing rewrites over our representation.
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