{"ID":2887497,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01180","arxiv_id":"2508.01180","title":"A Dynamic Allocation Scheme for Adaptive Shared-Memory Mapping on Kilo-core RV Clusters for Attention-Based Model Deployment","abstract":"Attention-based models demand flexible hardware to manage diverse kernels with varying arithmetic intensities and memory access patterns. Large clusters with shared L1 memory, a common architectural pattern, struggle to fully utilize their processing elements (PEs) when scaled up due to reduced throughput in the hierarchical PE-to-L1 intra-cluster interconnect. This paper presents Dynamic Allocation Scheme (DAS), a runtime programmable address remapping hardware unit coupled with a unified memory allocator, designed to minimize data access contention of PEs onto the multi-banked L1. We evaluated DAS on an aggressively scaled-up 1024-PE RISC-V cluster with Non-Uniform Memory Access (NUMA) PE-to-L1 interconnect to demonstrate its potential for improving data locality in large parallel machine learning workloads. For a Vision Transformer (ViT)-L/16 model, each encoder layer executes in 5.67 ms, achieving a 1.94x speedup over the fixed word-level interleaved baseline with 0.81 PE utilization. Implemented in 12nm FinFET technology, DAS incurs \u003c0.1 % area overhead.","short_abstract":"Attention-based models demand flexible hardware to manage diverse kernels with varying arithmetic intensities and memory access patterns. Large clusters with shared L1 memory, a common architectural pattern, struggle to fully utilize their processing elements (PEs) when scaled up due to reduced throughput in the hierar...","url_abs":"https://arxiv.org/abs/2508.01180","url_pdf":"https://arxiv.org/pdf/2508.01180v1","authors":"[\"Bowen Wang\",\"Marco Bertuletti\",\"Yichao Zhang\",\"Victor J. B. Jung\",\"Luca Benini\"]","published":"2025-08-02T03:42:54Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false}