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SKLP, Institute of Computing Technology, Chinese Academy of Sciences, Hong Kong Polytechnic University, K [16]. Following DeiT [72], we develop three variants of BinaryAttention, namely -T (tiny), -S (small) and -B (base), by substituting all standard attention modules with BinaryAttention. We follow the experimental settings in DeiT [72], which are detailed in supplementary file. The models are fine-tuned with the self-distillation [34] strategy, where the full-precision counterparts serve as the teacher. We compare with quantization based methods PTQ, 脳\times and 1.
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Wafer-scale SRAM CIM can deliver up to 17x better energy efficiency for LLM inference by eliminating off-chip data movement and using token-grained pipelining.
