# Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables

arXiv: https://arxiv.org/abs/2511.05985 · HTML: https://mlsanity.com/paper/2511.05985/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 8 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [A multimodal sensor dataset for continuous stress detection of nurses in a hospital](https://mlsanity.com/paper/2108.07689/) (2021)
- [Cross-Layer Approximation For Printed Machine Learning Circuits](https://mlsanity.com/paper/2203.05915/) (2022)
- [Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits](https://mlsanity.com/paper/2302.14576/) (2023)
- [GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data](https://mlsanity.com/paper/2005.01834/) (2020)
- [Approximate Computing and the Efficient Machine Learning Expedition](https://mlsanity.com/paper/2210.00497/) (2022)
- [B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows](https://mlsanity.com/paper/2101.10870/) (2021)
- [Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons](https://mlsanity.com/paper/2312.17612/) (2023)
- [Embedding Hardware Approximations in Discrete Genetic-based Training for Printed MLPs](https://mlsanity.com/paper/2402.02930/) (2024)
- [Evolutionary Approximation of Ternary Neurons for On-sensor Printed Neural Networks](https://mlsanity.com/paper/2407.20589/) (2024)
- [Support Vector Machines Classification on Bendable RISC-V](https://mlsanity.com/paper/2508.19656/) (2025)
- [Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables](https://mlsanity.com/paper/2508.19661/) (2025)

## Abstract (arXiv)

Flexible electronics offer unique advantages for conformable, lightweight, and disposable healthcare wearables. However, their limited gate count, large feature sizes, and high static power consumption make on-body machine learning classification highly challenging. While existing bendable RISC-V systems provide compact solutions, they lack the energy efficiency required. We present a mechanically flexible RISC-V that integrates a bespoke multiply-accumulate co-processor with fixed coefficients to maximize energy efficiency and minimize latency. Our approach formulates a constrained programming problem to jointly determine co-processor constants and optimally map Multi-Layer Perceptron (MLP) inference operations, enabling compact, model-specific hardware by leveraging the low fabrication and non-recurring engineering costs of flexible technologies. Post-layout results demonstrate near-real-time performance across several healthcare datasets, with our circuits operating within the power budget of existing flexible batteries and occupying only 2.42 mm^2, offering a promising path toward accessible, sustainable, and conformable healthcare wearables. Our microprocessors achieve an average 2.35x speedup and 2.15x lower energy consumption compared to the state of the art.