Computing with a Chemical Reservoir

Scientific computing, data analytics and artificial intelligence (in particular with the proliferation of large language models) are driving an explosive growth in computing needs. However, leading-edge high-performance computing systems composed of digital CMOS-based processing elements are reaching physical limits that do not allow any more significant gains in energy efficiency. As we progress towards post-exascale computing systems disruptive approaches to break this barrier in energy efficiency are required. Novel analog and hybrid digital-analog systems promise improvements in energy efficiency of orders of magnitude. Among the various solutions under exploration, biochemical computing has the potential to enable a new type of computing devices with immense computational power. These device can harness the efficiency of biological cells in solving optimization problems (chemical reactions naturally converge to optimal steady states) and are scalable by considering increasingly larger reaction systems or vessels, potentially meeting the high-performance requirements of scientific computing. However, several theoretical and practical limitations still exists going from how we formulate and map problems to chemical reaction networks (CRNs) to how we should implement actual chemical reaction computing devices. In this paper, we propose a framework for chemical computation using biochemical systems and present initial components of our approach: an abstract chemical reaction dialect, implemented as a multi-level intermediate representation (MLIR) compiler extension, and a path for representing mathematical problems with CRNs. We demonstrate the potential of this approach by emulating a simplified chemical reservoir device. This work lays the foundation for leveraging chemistry’s computing power to create energy-efficient, high-performance computing systems for contemporary computing needs.