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Found 6 papers, 0 papers with code
Heterogeneity-Aware Asynchronous Decentralized Training
Is it possible to get the best of both worlds - designing a distributed training method that has both high performance as All-Reduce in homogeneous environment and good heterogeneity tolerance as AD-PSGD?
Hop: Heterogeneity-Aware Decentralized Training
Based on a unique characteristic of decentralized training that we have identified, the iteration gap, we propose a queue-based synchronization mechanism that can efficiently implement backup workers and bounded staleness in the decentralized setting.
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
In this paper, inspired by recent work in machine learning systems, we propose a solution HyPar to determine layer-wise parallelism for deep neural network training with an array of DNN accelerators.
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
It is a challenging task to have real-time, efficient, and accurate hardware RNN implementations because of the high sensitivity to imprecision accumulation and the requirement of special activation function implementations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices
As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy.
GraphR: Accelerating Graph Processing Using ReRAM
GRAPHR gains a speedup of 1. 16x to 4. 12x, and is 3. 67x to 10. 96x more energy efficiency compared to PIM-based architecture.
Distributed, Parallel, and Cluster Computing Hardware Architecture
