Tin-Tin: Towards Tiny Learning on Tiny Devices with Integer-based Neural Network Training
This addresses the challenge of enabling machine learning on low-power edge devices for applications like intelligent sensing, though it is incremental as it builds on integer-based methods for specific hardware constraints.
The paper tackles the problem of deploying machine learning on resource-constrained microcontrollers by proposing Tin-Tin, an integer-based on-device training framework, which enables energy-efficient and memory-optimized lifelong learning on tiny devices.
Recent advancements in machine learning (ML) have enabled its deployment on resource-constrained edge devices, fostering innovative applications such as intelligent environmental sensing. However, these devices, particularly microcontrollers (MCUs), face substantial challenges due to limited memory, computing capabilities, and the absence of dedicated floating-point units (FPUs). These constraints hinder the deployment of complex ML models, especially those requiring lifelong learning capabilities. To address these challenges, we propose Tin-Tin, an integer-based on-device training framework designed specifically for low-power MCUs. Tin-Tin introduces novel integer rescaling techniques to efficiently manage dynamic ranges and facilitate efficient weight updates using integer data types. Unlike existing methods optimized for devices with FPUs, GPUs, or FPGAs, Tin-Tin addresses the unique demands of tiny MCUs, prioritizing energy efficiency and optimized memory utilization. We validate the effectiveness of Tin-Tin through end-to-end application examples on real-world tiny devices, demonstrating its potential to support energy-efficient and sustainable ML applications on edge platforms.