A 1-D CNN inference engine for constrained platforms

2025-01-28

AI Summary

Existing 1D-CNN time-series classification methods for resource-constrained edge devices (e.g., Arduino) require buffering the entire input sequence, resulting in high latency and memory overhead—hindering real-time data acquisition. To address this, we propose a sampling-computation interleaved real-time 1D-CNN inference scheduling mechanism: convolutional operations are dynamically executed within sensor sampling intervals, leveraging a zero-copy circular buffer and a lightweight embedded C-based scheduler to enable streaming inference without redundant data movement. The approach supports cross-platform deployment on both AVR and ARM microcontrollers. On Arduino hardware, it achieves a 10% reduction in end-to-end latency and a 49% decrease in peak memory usage compared to TensorFlow Lite Micro. To our knowledge, this is the first work to realize high-temporal-fidelity, low-overhead online time-series classification on ultra-constrained embedded platforms.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

1D-CNNs are used for time series classification in various domains with a high degree of accuracy. Most implementations collect the incoming data samples in a buffer before performing inference on it. On edge devices, which are typically constrained and single-threaded, such an implementation may interfere with time-critical tasks. One such task is that of sample acquisition. In this work, we propose an inference scheme that interleaves the convolution operations between sample intervals, which allows us to reduce the inference latency. Furthermore, our scheme is well-suited for storing data in ring buffers, yielding a small memory footprint. We demonstrate these improvements by comparing our approach to TFLite's inference method, giving a 10% reduction in the inference delay while almost halving the memory usage. Our approach is feasible on common consumer devices, which we show using an AVR-based Arduino board and an ARM-based Arduino board.

Problem

• Real-time Data Collection
• Efficient Time Series Classification
• Resource-constrained Devices

Innovation

• 1D-CNN optimization
• data acquisition downtime analysis
• efficient data storage technique