Adaptive Computing in Memory Meets Conventional Batteryless Platforms

Computing In-Memory (CIM) with emerging nonvolatile memory (NVM) technologies is promising for batteryless systems since it removes the need for explicit backup and energy-hungry data transfer between the processor and memory. However, existing CIM solutions are not effective in accelerating memory-bound inference tasks efficiently on batteryless systems. They operate at relatively low frequencies, complicate application development, and do not consider energy harvesting dynamics to optimize their throughput. To address the issues, this paper presents a novel CIM-based batteryless computing platform, called Viadotto, that provides efficient and adaptive acceleration for memory-bound computing workloads. Viadotto meets adaptive CIM and microcontroller-based (MCU-based) conventional batteryless platforms for the first time. Basically, Viadotto exposes a programming model supported by its compiler and a pipelined memory controller, which hides low-level CIM operations from applications. Furthermore, its runtime issues CIM operations in an energy-efficient manner and optimizes throughput in a programmer-transparent way by adapting CIM parallelism to react to ambient power dynamics. Our evaluation shows that Viadotto outperforms existing CIM solutions for batteryless systems by 48%.