# BenDi: An Energy-Efficient Quasi-Stochastic Systolic Architecture for Edge Bioelectronics

arXiv: https://arxiv.org/abs/2606.12235 · HTML: https://mlsanity.com/paper/2606.12235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [DeepScaleTool : A Tool for the Accurate Estimation of Technology Scaling in the Deep-Submicron Era](https://mlsanity.com/paper/2102.10195/) (2021)
- [DiP: A Scalable, Energy-Efficient Systolic Array for Matrix Multiplication Acceleration](https://mlsanity.com/paper/2412.09709/) (2024)
- [Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing](https://mlsanity.com/paper/1706.02344/) (2017)
- [OISMA: On-the-fly In-memory Stochastic Multiplication Architecture for Matrix-Multiplication Workloads](https://mlsanity.com/paper/2508.08822/) (2025)
- [DISCA: A Digital In-memory Stochastic Computing Architecture Using A Compressed Bent-Pyramid Format](https://mlsanity.com/paper/2511.17265/) (2025)

## Abstract (arXiv)

Continuous long-term monitoring and diagnosis of biomedical signals, such as electrocardiograms (ECGs), can help mitigate an increasing threat to public health. Artificial Intelligence (AI) models, such as Convolutional Neural Networks (CNNs), provide accurate monitoring and classification for relevant diseases; however, they require more computational resources than conventional AI hardware can typically afford, especially for a resource-constrained environment on the edge. In this work, we present BenDi, an energy-efficient quasi-stochastic systolic architecture for bioelectronic systems on the edge. BenDi leverages multiple levels of energy and power optimization, ranging from circuits to software quantization, including low supply voltage, the underlineBent-Pyramid data format for quasi-stochastic multiplication, the underlineDiP systolic dataflow, and hardware-aware quantization, to handle CNNs with high accuracy on the edge within limited hardware budgets. The hardware implementation results, using a commercial 22nm technology, show that BenDi architecture, at 0.5 Voltage and 100 MHz, offers 3.35x smaller area and 5x higher energy efficiency, compared to state-of-the-art binary-based weight-stationary systolic architectures. Regarding Bioelectronic edge systems, BenDi achieves an order-of-magnitude improvement in energy efficiency and another order-of-magnitude improvement in area efficiency, compared to its counterparts. This significant improvement comes at the cost of 1% to 3.3% accuracy loss on the MIT-BIH and Apnea-ECG benchmarks, respectively, compared with conventional computing using the 32-bit floating-point format.