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01/29/2023
Pipe-BD: Pipelined Parallel Blockwise Distillation
Training large deep neural network models is highly challenging due to t...
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01/25/2023
SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators
Graph convolutional networks (GCNs) are becoming increasingly popular as...
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01/24/2023
Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression
In training of modern large natural language processing (NLP) models, it...
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01/24/2023
Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators
Graph convolutional networks (GCNs) are becoming increasingly popular as...
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01/23/2023
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration
Co-exploration of an optimal neural architecture and its hardware accele...
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01/23/2023
SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs
Sequence alignment forms an important backbone in many sequencing applic...
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03/31/2022
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
Model quantization is considered as a promising method to greatly reduce...
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11/04/2021
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
Model quantization is known as a promising method to compress deep neura...
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09/14/2020