Semantic-Driven Hardware Specialization: From Language Semantics to Accelerator Design

There's this fascinating disconnect in current hardware-software co-design. Papers like Schmidt and Becker (2024) and Palomero Bernardo et al. (2025) talk about compiler-driven hardware design, but they're still thinking in terms of traditional computational patterns. Meanwhile, work like DimSum (Sammler et al., 2023) shows how important precise semantics are for multi-language systems. What if we flipped the design process entirely? Instead of starting with "what computations do we want to accelerate?" we'd start with "what language semantics do we want to preserve?" My idea is to develop a methodology that takes formal language semantics as input and generates specialized hardware that implements those semantics directly. For example, given a formal semantics for Rust's ownership system, we could design hardware that enforces those rules with zero runtime overhead. Or given semantics for a quantum programming language, we could generate hardware that natively implements those quantum operations without translation layers. This connects to Chetioui et al.'s (2022) work on the P3 problem but takes it further - instead of just making code portable across existing architectures, we're generating new architectures tailored to specific language semantics. It could lead to processors that are "semantically aware" in a fundamental way, potentially eliminating entire classes of software overhead while maintaining language-level guarantees. The really exciting part is how this could transform the hardware-software boundary. Instead of compilers having to work around fixed hardware constraints, the hardware would be designed to perfectly match the language's abstractions. That's a paradigm shift that could deliver performance gains while actually making software simpler and more reliable.

References:

1. Ph.D. Project: Compiler-Driven Hardware/Software Co- Design for Embedded AI. Patrick Schmidt, Jürgen Becker (2024). IEEE Symposium on Field-Programmable Custom Computing Machines.
2. Compiler-aware AI Hardware Design for Edge Devices. Paul Palomero Bernardo, Patrick Schmid, Christoph Gerum, Oliver Bringmann (2025). EdgeSys@EuroSys.
3. DimSum: A Decentralized Approach to Multi-language Semantics and Verification. Michael Sammler, Simon Spies, Youngju Song, Emanuele D’Osualdo, Robbert Krebbers, Deepak Garg, Derek Dreyer (2023). Proc. ACM Program. Lang..
4. P3 problem and Magnolia language: Specializing array computations for emerging architectures. Benjamin Chetioui, Marius Kleppe Larnøy, Jaakko Järvi, M. Haveraaen, L. Mullin (2022). Frontiers of Computer Science.