POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging

Abstract: Fine-tuning models on edge devices like mobile phones would enable privacy-preserving personalization over sensitive data. However, edge training has historically been limited to relatively small models with simple architectures because training is both memory and energy intensive. We present POET, an algorithm to enable training large neural networks on memory-scarce battery-operated edge devices. POET jointly optimizes the integrated search search spaces of rematerialization and paging, two algorithms to reduce the memory consumption of backpropagation. Given a memory budget and a run-time constraint, we formulate a mixed-integer linear program (MILP) for energy-optimal training. Our approach enables training significantly larger models on embedded devices while reducing energy consumption while not modifying mathematical correctness of backpropagation. We demonstrate that it is possible to fine-tune both ResNet-18 and BERT within the memory constraints of a Cortex-M class embedded device while outperforming current edge training methods in energy efficiency. POET is an open-source project available at https://github.com/ShishirPatil/poet

• The paper presents POET, which integrates rematerialization and paging to enable energy-efficient training of large models on resource-limited devices.
• It employs a mixed-integer linear programming formulation to optimize memory usage while maintaining model accuracy under strict constraints.
• Empirical evaluations on platforms from Cortex M0 to Jetson TX2 show up to 35% energy savings compared to existing methods.

An Analysis of POET: Enabling Training of Large Neural Networks on Edge Devices

The paper "POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging" presents an innovative approach to facilitating the training of large neural network models on resource-constrained edge devices. Edge devices, such as smartphones and microcontrollers, typically have limited memory and processing power, which historically constrained them to performing inference with pre-trained models rather than being capable of training. The novel methodology allows for fine-tuning extensive models like ResNet-18 and BERT directly on these devices by optimizing energy consumption through a mixed approach of rematerialization and paging.

Edge training, particularly where sensitive data is involved, offers significant potential for privacy-preserving personalization. Previous approaches relied heavily on cloud-based training due to the substantial memory and energy requirements. This paper's contribution is significant as it tackles the limitations of memory and energy consumption that have traditionally restricted edge training to smaller, simpler models.

The authors introduce POET, an algorithm that reconciles rematerialization and paging to create an energy-efficient training environment on limited memory devices. By formulating this task as a mixed-integer linear programming (MILP) problem, POET optimizes the integrated search spaces of rematerialization—an approach where activations are recomputed rather than stored—and paging, which involves moving data to and from secondary storage.

Strong Numerical Performance and Evaluation

The strength of POET lies in its empirical evaluation, which demonstrates its superior energy efficiency compared to pre-existing methods. It shows the capacity of training large models like ResNet-18 and BERT on platforms varying in computational limits from Cortex M0 to Nvidia's Jetson TX2. Across these diverse platforms, POET consistently reduced the energy consumption required for model training, achieving up to 35% less energy usage compared to competitive methods like Checkmate and DTR.

The tests reveal that by fine-tuning the balance between rematerialization and paging, POET's solutions sustain model accuracy while complying with stringent memory and runtime constraints. This is noteworthy because it proves the feasibility of performing complex model updates even under tight memory budgets without compromising performance or efficiency.

Implications and Future Directions

The theoretical and practical implications of this research are manifold. Practically, it paves the way for broader deployment of sophisticated AI models directly on edge devices, which could transform how applications handle personalized data. Theoretically, the paper showcases how emerging constraints (e.g., memory, energy) in computing can be addressed through elegant mathematical formulations.

The authors mention the possibility of embedding further methods like activation compression into POET's optimization pipeline, suggesting that exploring more integrated memory management techniques could yield even greater efficiency gains. As privacy continues to be a primary concern in data processing, enabling more robust and versatile edge computing tactics becomes crucial.

In future developments, extending this methodology to encompass wider model types and further optimizing the MILP formulations to solve even faster or at lower energy costs could significantly impact both industry practices and academic pursuits in edge AI. Furthermore, as device architectures evolve and diversify, adaptive solutions like POET that can cater to a variety of configurations will be critical in sustaining growth in edge AI capabilities. This paper stands as a substantial contribution towards marrying computational efficiency with practical deployment necessities in edge computing.