← 研踪 PaperTrailarXiv:2611.13644 · v1
Tri-Stack-PIM: A Heterogeneous Multi-Die 3D-Integrated Processing-in-Memory Architecture for Efficient LLM Inference
首次提交 2026-11-10 14:40
版本更新 2026-11-10 14:40
雷达观测 2026-11-11 11:04
摘要
Large language model (LLM) inference during autoregressive decoding is fundamentally memory-bandwidth-bound. Existing three-dimensional (3D) stacked processing-in-memory (PIM) architectures alleviate the off-chip bandwidth wall, yet remain constrained by several limitations: physical layer (PHY), controllers, and direct memory access (DMA) engines compete with arithmetic units for advanced-node base-die area; hybrid-bonding keep-out zones fragment the base-die floorplan and impede physical design; compute tiles built in a dynamic random access memory (DRAM) process are restricted to low logic density, capping achievable throughput; and nonlinear operations offloaded to the host reintroduce a communication bottleneck. We present Tri-Stack-PIM, a heterogeneous three-die architecture that resolves these tensions by dedicating a top DRAM die to storage, a middle buffer die on a mature process node to PHYs, DMA engines, and near-memory compute units, and a bottom base die on an advanced node exclusively to single-instruction-multiple-data (SIMD) multiply-accumulate (MAC) arrays. We further introduce a Unified Buffer-Aware Ready-based (UBAR) hardware out-of-order micro-operation scheduler that enforces cross-die dependency correctness at runtime while maximizing issue-slot utilization through a multi-term priority function, recovering up to 90% of the cycles that an in-order policy would forfeit. A joint architecture-dataflow co-exploration identifies Pareto-optimal configurations that reduce the energy-delay product (EDP) by 33-66x over naive parameter selection. Evaluated across multi-head, grouped-query, and multi-query attention (MHA, GQA, MQA) mechanisms with batch sizes 1-32, Tri-Stack-PIM achieves geometric-mean speedups of 4.95x, 2.57x, and 1.68x across all three attention mechanisms and batch sizes over a graphics processing unit (GPU) baseline, AttAcc, and H2-LLM, respectively.
作者备注：11 pages, 9 figures
观测到的版本
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v1 · 2026-11-10 14:40 · 最新观测版本
Tri-Stack-PIM: A Heterogeneous Multi-Die 3D-Integrated Processing-in-Memory Architecture for Efficient LLM Inference
Large language model (LLM) inference during autoregressive decoding is fundamentally memory-bandwidth-bound. Existing three-dimensional (3D) stacked processing-in-memory (PIM) architectures alleviate the off-chip bandwidth wall, yet remain constrained by several limitations: physical layer (PHY), controllers, and direct memory access (DMA) engines compete with arithmetic units for advanced-node base-die area; hybrid-bonding keep-out zones fragment the base-die floorplan and impede physical design; compute tiles built in a dynamic random access memory (DRAM) process are restricted to low logic density, capping achievable throughput; and nonlinear operations offloaded to the host reintroduce a communication bottleneck. We present Tri-Stack-PIM, a heterogeneous three-die architecture that resolves these tensions by dedicating a top DRAM die to storage, a middle buffer die on a mature process node to PHYs, DMA engines, and near-memory compute units, and a bottom base die on an advanced node exclusively to single-instruction-multiple-data (SIMD) multiply-accumulate (MAC) arrays. We further introduce a Unified Buffer-Aware Ready-based (UBAR) hardware out-of-order micro-operation scheduler that enforces cross-die dependency correctness at runtime while maximizing issue-slot utilization through a multi-term priority function, recovering up to 90% of the cycles that an in-order policy would forfeit. A joint architecture-dataflow co-exploration identifies Pareto-optimal configurations that reduce the energy-delay product (EDP) by 33-66x over naive parameter selection. Evaluated across multi-head, grouped-query, and multi-query attention (MHA, GQA, MQA) mechanisms with batch sizes 1-32, Tri-Stack-PIM achieves geometric-mean speedups of 4.95x, 2.57x, and 1.68x across all three attention mechanisms and batch sizes over a graphics processing unit (GPU) baseline, AttAcc, and H2-LLM, respectively.
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