An Instruction Set Architecture for IMPLY-based Memristive Processing-in-Array
2026-06-25 • Emerging Technologies
Emerging TechnologiesHardware Architecture
AI summaryⓘ
The authors explore a new type of tiny computer chip designed to use very little energy for edge devices, like smart sensors. Instead of traditional parts that separate memory and processing, their design uses memristors that store data and compute in the same place, saving power. They created a custom instruction set based on a standard method, showed how the chip works using simulations, and compared its energy use to usual microcontrollers. To prove it's practical, they explain how it could be used in an eco-friendly sensor system.
Edge computingUltra-low-powerMemristorIn-Memory Computing (IMC)IMPLY logicRV32I instruction setvon Neumann bottleneckMicrocontrollerMemristive crossbar arrayStatic power leakage
Authors
Liam Splittgerber, Fabian Seiler, Nima TaheriNejad
Abstract
The push towards expanded ultra-low-power edge computing necessitates hardware capable of operating under extremely strict energy constraints. Traditional Complementary Metal-Oxide-Semiconductor (CMOS) microcontrollers are fundamentally limited in this domain by the von Neumann bottleneck and by the static power leakage inherent to volatile memory. Memristive In-Memory Computing (IMC) offers a promising solution to these inefficiencies by unifying data storage and computation into a single non-volatile component. However, the State of the Art (SoA) predominantly focuses on accelerators designed to be a co-processor for data-intensive computation. This leaves the prospect of standalone, general-purpose IMC microcontroller architectures underexplored. This thesis proposes such an architecture tailored for ultra-low-power edge devices, alongside an instruction set closely derived from the RV32I standard. Using the IMPLY stateful logic paradigm, a complete implementation of the proposed instruction set is provided, and the novel addressing schema required to support computation in the memristive crossbar array is described as well. Then, the energy use and other circuit-level metrics of the proposed architecture are evaluated through simulation and compared against those of traditional microcontrollers. Finally, the functional viability of the design is demonstrated through an application case study, describing how the proposed design could be used in an intelligent environmental sensor node.