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cs.LG2024★ 1 cited
Beyond 2:4: exploring V:N:M sparsity for efficient transformer inference on GPUs
Kang Zhao, Tao Yuan, Han Bao +6
To date, 2:4 sparsity has stood as the only sparse pattern that can be accelerated using sparse tensor cores on GPUs. In practice, 2:4 sparsity often possesses low actual speedups…
cs.LG2024★ 1 cited
1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit
Chang Gao, Jianfei Chen, Kang Zhao +2
Fully quantized training (FQT) accelerates the training of deep neural networks by quantizing the activations, weights, and gradients into lower precision. To explore the ultimate…