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Meng Li

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Preprint Jul 2026

HEMERA: A Heterogeneous Memory-Centric Accelerator with Recursive Dataflow for Edge-Constrained State-Space-Duality Models Inference

Structured State Space Models (SSMs), such as Mamba, enable efficient long-sequence modeling with linear time complexity. Recent implementations realize this capability through Structured State Space Duality (SSD), which transforms recursive state evolution into matrix-form computations. However, SSD introduces substantial system-level overheads, including quadratic intermediate materialization, irregular data movement, and prefix-dependent execution, leading to excessive memory traffic and bandwidth demand on conventional architectures. Although prior accelerators mitigate these overheads through optimized dataflows or compute-in-memory techniques, they largely retain matrix-oriented SSD execution and cannot simultaneously avoid quadratic intermediate storage and efficiently map dependency-bound state propagation. This paper presents HEMERA, a heterogeneous memory-centric accelerator for efficient Mamba-2 inference. Rather than directly executing the matrix-form SSD computation, HEMERA reformulates it into an algebraically equivalent streaming-recursive dataflow that avoids quadratic intermediate storage while preserving the original computation. The resulting heterogeneous execution paradigm maps dense linear operations onto in-memory computing units and recursive state updates onto a dedicated streaming engine. Across Mamba-2 models ranging from 130M to 2.8B, HEMERA achieves average latency speedups of 1.4x-3.6x and energy-efficiency improvements of 12.2x-27.0x over the official optimized fused Mamba-2 kernel on NVIDIA A100. It further reduces the average SSD-related execution-time ratio across model scales to 14.12% during long-sequence inference, demonstrating its potential for efficient deployment under edge constraints.

Hao Ding, Ling Liang, Ruitong Qiao et al. · 0 citations
Jun 2026

PipeComm: Maximizing Link Utilization Through Pipeline-Aware Collective Communication Synthesis

Efficient collective communication is crucial for distributed training. While recent topology-aware synthesis approaches attempt to optimize communication based on the network topology, they struggle with heterogeneous environments where links exhibit distinct bandwidths and asymmetric connections. The main inefficiency arises from single-round communication: toward the end of a communication phase, only a few remaining transfers occupy limited links, leaving most network channels idle. Furthermore, existing methods fail to handle the congestion that naturally occurs under overlapping execution, leading to significant bandwidth underutilization. To address these challenges, we propose PipeComm, a pipelineaware communication synthesis framework that maximizes bandwidth utilization by intelligently overlapping multiple data chunks. By explicitly modeling pipeline behavior, our method enables congestion-free scheduling across iterations and efficiently utilizes heterogeneous links. We develop an optimal synthesis method for constructing high-quality communication patterns and introduce a complementary incremental strategy that significantly improves scalability for large topologies. Experimental results show that the optimal strategy achieves over a 1.39× speedup compared to the state-of-the-art communication methods. Moreover, PipeComm supports diverse collective operations, demonstrating both efficiency and generality.

Ruifan Xu, Yuze Luo, Yuhao Meng et al. · 0 citations