Convergence Guarantees Established for Asynchronous Pipeline Training on Analog In‑Memory Accelerators

Global: Convergence Guarantees for Asynchronous Pipeline Training on Analog In‑Memory Accelerators

Researchers who authored the arXiv preprint (arXiv:2410.15155v2) have presented a theoretical analysis of stochastic gradient descent (SGD) applied to large deep neural networks (DNN) trained on analog in‑memory computing (AIM C) hardware using an asynchronous pipeline architecture. The study, posted in October 2024, seeks to explain how such hardware can maintain training efficiency while addressing the unique challenges posed by analog weight storage.

Background on Analog In‑Memory Computing

Analog in‑memory computing keeps model weights physically resident in memory cells, eliminating the need to transfer data between separate processor and memory units. This design reduces energy consumption and latency, offering a promising path for scaling the training of massive DNNs.

Challenges of Scaling AIMC Systems

Despite its efficiency, scaling AIMC accelerators is hampered by the high cost and inaccuracy of weight copying, which makes traditional data‑parallel strategies less effective. Consequently, researchers have turned to pipeline parallelism to keep multiple accelerators engaged throughout the training process.

Asynchronous Pipeline Parallelism

The asynchronous pipeline approach allows different stages of the training pipeline to operate concurrently, but it introduces stale‑weight issues because weight updates may no longer correspond to current gradients. The authors note that this discrepancy can undermine the validity of gradient information.

Theoretical Findings

According to the authors, their analysis demonstrates that the proposed Analog‑SGD‑AP algorithm converges with an iteration complexity of O(ε⁻² + ε⁻¹). This bound matches the complexity of conventional digital SGD and of synchronous analog SGD, differing only by a non‑dominant O(ε⁻¹) term.

Practical Implications

The results suggest that asynchronous pipelining can be employed on AIMC hardware with little to no additional theoretical cost, effectively allowing computation and communication to overlap without sacrificing convergence speed.

Future Directions

The authors acknowledge that empirical validation on physical AIMC prototypes remains necessary, and they propose extending the framework to cover a broader range of analog imperfections and network architectures.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.