Processing-In-Memory
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Related Articles from SNS
PALUTE: Processing-In-Memory Acceleration via Lookup Table for Edge LLM Inference
arXiv:2606.08891v1 Announce Type: new Abstract: Large language models are increasingly deployed on edge devices with tight power and area budgets. While mixed-precision GEMM reduces arithmetic complexity, quantized inference is often dominated by dequantization and nonlinear operators. Lookup Table (LUT)-based method mitigates these costs by precomputing outputs and replacing repeated arithmetic with table lookups, but existing designs incur significant capacity and lookup-latency overheads.
RH+: Row-Hit-Optimized Scheduling for PIM-based LLM Inference
arXiv:2606.05511v1 Announce Type: new Abstract: Large language model inference on processing-in-memory (PIM) architectures promises to break the memory wall by performing multiply-accumulate (MAC) operations directly within HBM3 DRAM banks. Prior work identifies the power constraint timing parameter nCCDAB as the primary performance bottleneck and optimizes scheduling accordingly. We demonstrate that for GEMV operations that dominate autoregressive decoding, the DRAM row cycle time (nRC) is...