# Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge

arXiv: https://arxiv.org/abs/2509.08193 · HTML: https://mlsanity.com/paper/2509.08193/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 114 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning](https://mlsanity.com/paper/2111.04724/) (2021)
- [MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI](https://mlsanity.com/paper/2410.12032/) (2024)
- [Cross-Layer Approximation For Printed Machine Learning Circuits](https://mlsanity.com/paper/2203.05915/) (2022)
- [Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems](https://mlsanity.com/paper/2306.13177/) (2023)
- [CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs](https://mlsanity.com/paper/2201.01863/) (2022)
- [Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers](https://mlsanity.com/paper/2301.11899/) (2023)
- [BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables](https://mlsanity.com/paper/2406.03886/) (2024)
- [Optimizing Energy Efficiency in Subthreshold RISC-V Cores](https://mlsanity.com/paper/2502.06588/) (2025)
- [Flexing RISC-V Instruction Subset Processors to Extreme Edge](https://mlsanity.com/paper/2505.04567/) (2025)

## Abstract (arXiv)

We present FlexiFlow, a lifetime-aware design framework for item-level intelligence (ILI) where computation is integrated directly into disposable products like food packaging and medical patches. Our framework leverages natively flexible electronics which offer significantly lower costs than silicon but are limited to kHz speeds and several thousands of gates. Our insight is that unlike traditional computing with more uniform deployment patterns, ILI applications exhibit 1000X variation in operational lifetime, fundamentally changing optimal architectural design decisions when considering trillion-item deployment scales. To enable holistic design and optimization, we model the trade-offs between embodied carbon footprint and operational carbon footprint based on application-specific lifetimes. The framework includes: (1) FlexiBench, a workload suite targeting sustainability applications from spoilage detection to health monitoring; (2) FlexiBits, area-optimized RISC-V cores with 1/4/8-bit datapaths achieving 2.65X to 3.50X better energy efficiency per workload execution; and (3) a carbon-aware model that selects optimal architectures based on deployment characteristics. We show that lifetime-aware microarchitectural design can reduce carbon footprint by 1.62X, while algorithmic decisions can reduce carbon footprint by 14.5X. We validate our approach through the first tape-out using a PDK for flexible electronics with fully open-source tools, achieving 30.9kHz operation. FlexiFlow enables exploration of computing at the Extreme Edge where conventional design methodologies must be reevaluated to account for new constraints and considerations.