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BBS: Bi-directional Bit-level Sparsity for Deep Learning Acceleration by Chen, Yuzong; Meng, Jian; Seo, Jae-sun; Abdelfattah, Mohamed S. is a scholarly article available to read on EtoBox.

What is BBS: Bi-directional Bit-level Sparsity for Deep Learning Acceleration about?

Bit-level sparsity methods skip ineffectual zero-bit operations and are typically applicable within bit-serial deep learning accelerators. This type of sparsity at the bit-level is especially interesting because it is both orthogonal and compatible with other deep neural network (DNN) efficiency methods such as quantization and pruning. In this work, we improve the practicality and efficiency of bitlevel sparsity through a novel algorithmic bit-pruning, averaging, and compression method, and a co-designed efficient bit-serial hardware accelerator. On the algorithmic side, we introduce bidirectional bit sparsity (BBS). The key insight of BBS is that we can leverage bit sparsity in a symmetrical way to prune either zero-bits or one-bits. This significantly improves the load balance of bit-serial computing and guarantees the level of sparsity to be more than 50%. On top of BBS, we further propose two bit-level binary pruning methods that require no retraining, and can be seamlessly applied to quantized DNNs. Combining binary pruning with a new tensor encoding scheme, BBS can both skip computation and reduce the memory footprint associated with bi-directional sparse bit columns. On the

Author

Chen, Yuzong; Meng, Jian; Seo, Jae-sun; Abdelfattah, Mohamed S.

Published

2024

Language

EN