3 research outputs found
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Deep learning recommendation models (DLRMs) are used across many
business-critical services at Facebook and are the single largest AI
application in terms of infrastructure demand in its data-centers. In this
paper we discuss the SW/HW co-designed solution for high-performance
distributed training of large-scale DLRMs. We introduce a high-performance
scalable software stack based on PyTorch and pair it with the new evolution of
Zion platform, namely ZionEX. We demonstrate the capability to train very large
DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup
in terms of time to solution over previous systems. We achieve this by (i)
designing the ZionEX platform with dedicated scale-out network, provisioned
with high bandwidth, optimal topology and efficient transport (ii) implementing
an optimized PyTorch-based training stack supporting both model and data
parallelism (iii) developing sharding algorithms capable of hierarchical
partitioning of the embedding tables along row, column dimensions and load
balancing them across multiple workers; (iv) adding high-performance core
operators while retaining flexibility to support optimizers with fully
deterministic updates (v) leveraging reduced precision communications,
multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we
develop and briefly comment on distributed data ingestion and other supporting
services that are required for the robust and efficient end-to-end training in
production environments
Template-update-based visual tracking for automatic cell manipulation in FluidFM
AbstractVisual tracking is an essential process for performing automated cell manipulation tasks. However, Real-time accurate cell tracking in FluidFM is challenging due to probe occlusion during manipulation. This study presents a correlation filtering-based tracking method for real-time, high-precision position tracking of cells and probes. In this method, a target template update strategy is proposed to realize cell tracking under probe occlusion, which abandons the use of pixel values in the occlusion area and reduces the interference of the occlusion area in model learning. Cell tracking and automated manipulation experiments were carried out to validate the proposed method. The Experimental results show that the proposed tracking method can accurately measure the positions of occlusion cells and probe. Furthermore, combined with the precise force control of the FluidFM’s probe, the automated cell Pick & Place experiments was accomplished with high accuracy and robustness