Abstract: Processing-In-Memory (PIM) architectures alleviate the memory bottleneck in the decode phase of large language model (LLM) inference by performing operations like GEMV and Softmax in memory.
Abstract: As AI workloads grow, memory bandwidth and access efficiency have become critical bottlenecks in high-performance accelerators. With increasing data movement demands for GEMM and GEMV ...
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