ALFRED: Virtual Memory for Intermittent Computing

TL;DR

ALFRED introduces a virtual memory abstraction for intermittent computing that automatically manages memory mapping, significantly reducing energy consumption and improving performance without requiring new programming models.

Contribution

ALFRED provides an automatic, compile-time virtual memory abstraction that simplifies programming and optimizes energy use in intermittent computing systems.

Findings

Reduces energy consumption by up to two orders of magnitude.

Enables faster workload completion due to energy efficiency.

Does not require new programming models or syntax.

Abstract

We present ALFRED: a virtual memory abstraction that resolves the dichotomy between volatile and non-volatile memory in intermittent computing. Mixed-volatile microcontrollers allow programmers to allocate part of the application state onto non-volatile main memory. Programmers are therefore to explore manually the trade-off between simpler management of persistent state against the energy overhead for non-volatile memory operations and intermittence anomalies due to re-execution of non-idempotent code. This approach is laborious and yields sub-optimal performance. We take a different stand with ALFRED: we provide programmers with a virtual memory abstraction detached from the specific volatile nature of memory and automatically determine an efficient mapping from virtual to volatile or non-volatile memory. Unlike existing works, ALFRED does not require programmers to learn a new programming model or language syntax, while the mapping is entirely resolved at compile-time, reducing the run-time energy overhead. We implement ALFRED through a series of program machine-level code transformations. Compared to existing systems, we demonstrate that ALFRED reduces energy consumption by up to two orders of magnitude given a fixed workload. This enables the workloads to finish sooner, as the use of available energy shifts from ensuring forward progress to useful application processing.