Gina Sohn

Gina Sohn, Genghan Zhang, Konstantin Hossfeld et al.

Dynamic behaviors are becoming prevalent in many tensor applications. In machine learning, for example, the input tensors are dynamically shaped or ragged, and data-dependent control flow is widely used in many models. However, the limited expressiveness of prior programming abstractions for spatial dataflow accelerators forces the dynamic behaviors to be implemented statically or lacks the visibility for performance-critical decisions. To address these challenges, we present the Streaming Tensor Program (STeP), a new streaming abstraction that enables dynamic tensor workloads to run efficiently on spatial dataflow accelerators. STeP introduces flexible routing operators, an explicit memory hierarchy, and symbolic shape semantics that expose dynamic data rates and tensor dimensions. These capabilities unlock new optimizations-dynamic tiling, dynamic parallelization, and configuration time-multiplexing-that adapt to dynamic behaviors while preserving dataflow efficiency. Using a cycle-approximate simulator on representative LLM layers with real-world traces, dynamic tiling reduces on-chip memory requirement by 2.18x, dynamic parallelization improves latency by 1.5x, and configuration time-multiplexing improves compute utilization by 2.57x over implementations available in prior abstractions.

Jungwoo Kim, Rubens Lacouture, Genghan Zhang et al.

Mixture-of-Experts (MoE) has become a dominant architecture for scaling large language models (LLMs). However, the execution characteristics of MoE inference are changing rapidly and increasingly mismatch the assumptions underlying existing Processing-in-Memory (PIM) systems. Prior PIM systems for LLMs rely on static rules to offload memory-bound operations to PIM, without accounting for the combined effects of load imbalance and inter-GPU communication. Meanwhile, modern MoE models activate fewer experts out of increasingly many, creating a bimodal expert distribution: a small set of experts receives many tokens, while a long tail of experts receives only one or a few. We identify a trend in modern MoE models toward increasingly bimodal token-to-expert distributions, quantify the resulting disparity in arithmetic intensity across experts, and show that this disparity dramatically reduces the efficiency of state-of-the-art PIM systems for LLMs. To address this problem, we propose a scheduler for serving MoE models on multi-GPU systems with attached HBM-PIM stacks. Our scheduler partitions expert execution between GPU and PIM based on runtime token-to-expert distributions, while jointly considering interconnect overhead, memory bandwidth, GPU throughput, and PIM throughput. Moreover, we propose Sieve, a runtime framework that employs the scheduler to coordinate execution across GPUs and their attached HBM-PIM stacks. Sieve overlaps GPU computation, PIM computation, and intra- and inter-device communication while preserving cross-device dependencies induced by expert parallelism. Sieve is evaluated on our cycle-accurate simulator based on Ramulator 2.0. Compared to state-of-the-art PIM systems for MoE, Sieve improves both throughput and interactivity by 1.3x, 1.3x, and 1.6x on Qwen3.5-397B-A17B, GPT-OSS-120B, and Qwen3-30B-A3B, respectively.