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Found 12 papers, 3 papers with code
cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data
To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data.
Distributed, Parallel, and Cluster Computing
DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression
In this paper, we propose DeepSZ: an accuracy-loss bounded neural network compression framework, which involves four key steps: network pruning, error bound assessment, optimization for error bound configuration, and compressed model generation, featuring a high compression ratio and low encoding time.
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training, to allow training larger models or to accelerate training.
ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning
Moreover, compared with the state-of-the-art pruning-during-training approach, ClickTrain provides significant improvements both accuracy and compression ratio on the tested CNN models and datasets, under similar limited training time.
A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression
In this paper, we propose a novel memory-driven high performance DNN training framework that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger networks.
Perception Through 2D-MIMO FMCW Automotive Radar Under Adverse Weather
Millimeter-wave (mmWave) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) features that require accurate location and Doppler velocity estimates of objects, independent of environmental conditions.
Exploring Autoencoder-based Error-bounded Compression for Scientific Data
(1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model.
Efficient PHY Layer Abstraction under Imperfect Channel Estimation
As most existing work investigate the PHY layer abstraction under an assumption of perfect channel estimation, it may become unreliable if there exists channel estimation error in a real communication system.
SOLAR: A Highly Optimized Data Loading Framework for Distributed Training of CNN-based Scientific Surrogates
Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24. 4X speedup over PyTorch Data Loader and 3. 52X speedup over state-of-the-art data loaders.
Mutual Interference Mitigation for MIMO-FMCW Automotive Radar
We also derive analytical closed-form expressions for probabilities of detection and false alarm.
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data.
GWLZ: A Group-wise Learning-based Lossy Compression Framework for Scientific Data
Leveraging a group of neural networks, GWLZ significantly enhances the decompressed data reconstruction quality with negligible impact on the compression efficiency.
