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cs.LG2024
IM-Unpack: Training and Inference with Arbitrarily Low Precision Integers
Zhanpeng Zeng, Karthikeyan Sankaralingam, Vikas Singh
GEneral Matrix Multiply (GEMM) is a central operation in deep learning and corresponds to the largest chunk of the compute footprint. Therefore, improving its efficiency is an acti…
cs.LG2024
FrameQuant: Flexible Low-Bit Quantization for Transformers
Harshavardhan Adepu, Zhanpeng Zeng, Li Zhang +1
Transformers are the backbone of powerful foundation models for many Vision and Natural Language Processing tasks. But their compute and memory/storage footprint is large, and so,…