Mapi-Pro: An Energy Efficient Memory Mapping Technique for Intermittent Computing

TL;DR

This paper introduces Mapi-Pro, an ILP-based memory mapping technique that optimizes energy efficiency in intermittent IoT devices by intelligently allocating application data between SRAM and non-volatile memory, significantly reducing energy-delay product.

Contribution

It presents a novel ILP-based memory mapping approach specifically designed for energy-efficient intermittent computing in IoT devices, outperforming existing configurations.

Findings

38.10% less EDP than baseline under stable power

15.97% less EDP than baseline under unstable power

Effective during frequent power failures

Abstract

Battery-less technology evolved to replace battery usage in space, deep mines, and other environments to reduce cost and pollution. Non-volatile memory (NVM) based processors were explored for saving the system state during a power failure. Such devices have a small SRAM and large non-volatile memory. To make the system energy efficient, we need to use SRAM efficiently. So we must select some portions of the application and map them to either SRAM or FRAM. This paper proposes an ILP-based memory mapping technique for Intermittently powered IoT devices. Our proposed technique gives an optimal mapping choice that reduces the system's Energy-Delay Product (EDP). We validated our system using a TI-based MSP430FR6989 and MSP430F5529 development boards. Our proposed memory configuration consumes 38.10% less EDP than the baseline configuration and 9.30% less EDP than the existing work under stable power. Our proposed configuration achieves 15.97% less EDP than the baseline configuration and 21.99% less EDP than the existing work under unstable power. This work supports intermittent computing and works efficiently during frequent power failures.