Bladedisc: Optimizing dynamic shape machine learning workloads via compiler approach
Compiler optimization plays an increasingly important role to boost the performance of
machine learning models for data processing and management. With increasingly complex …
machine learning models for data processing and management. With increasingly complex …
[HTML][HTML] Adaptive partitioning and efficient scheduling for distributed DNN training in heterogeneous IoT environment
With the increasing proliferation of Internet-of-Things (IoT) devices, it is a growing trend
toward training a deep neural network (DNN) model in pipeline parallelism across resource …
toward training a deep neural network (DNN) model in pipeline parallelism across resource …
Recom: A compiler approach to accelerating recommendation model inference with massive embedding columns
Embedding columns are important for deep recommendation models to achieve high
accuracy, but they can be very time-consuming during inference. Machine learning (ML) …
accuracy, but they can be very time-consuming during inference. Machine learning (ML) …
TiMePReSt: Time and Memory Efficient Pipeline Parallel DNN Training with Removed Staleness
A Dutta, N Chaki, RK De - arXiv preprint arXiv:2410.14312, 2024 - arxiv.org
DNN training is extremely time-consuming, necessitating efficient multi-accelerator
parallelization, where a single iteration of training is split over the accelerators. Current …
parallelization, where a single iteration of training is split over the accelerators. Current …
A Comparative Study of Neural Network Compilers on ARMv8 Architecture
Abstract The deployment of Deep Neural Network (DNN) models in far edge devices is a
challenging task, because these devices are characterized by scarce resources. To address …
challenging task, because these devices are characterized by scarce resources. To address …