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Found 7 papers, 2 papers with code
Effective Interplay between Sparsity and Quantization: From Theory to Practice
In addition, through rigorous analysis, we demonstrate that sparsity and quantization are not orthogonal; their interaction can significantly harm model accuracy, with quantization error playing a dominant role in this degradation.
Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers
In this work, we study the effectiveness of existing sparse training recipes at \textit{high-sparsity regions} and argue that these methods fail to sustain the model quality on par with low-sparsity regions.
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.
TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings
For similar sized systems, it is ~4. 3x-4. 5x faster than the Graphcore IPU Bow and is 1. 2x-1. 7x faster and uses 1. 3x-1. 9x less power than the Nvidia A100.
STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
Recent innovations on hardware (e. g. Nvidia A100) have motivated learning N:M structured sparsity masks from scratch for fast model inference.
Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask
In this work, we focus on N:M sparsity and extensively study and evaluate various training recipes for N:M sparsity in terms of the trade-off between model accuracy and compute cost (FLOPs).
FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks
In contrast, FLAT unblocks transformer models for inputs with up to 64K elements
