标题： 基于注意力模型部署的千核RV集群自适应共享内存映射的动态分配方案 显示英文标题

标题： A Dynamic Allocation Scheme for Adaptive Shared-Memory Mapping on Kilo-core RV Clusters for Attention-Based Model Deployment

摘要： 基于注意力的模型需要灵活的硬件来管理具有不同计算强度和内存访问模式的多样内核。 具有共享L1内存的大规模集群，这是一种常见的架构模式，在扩展时由于层次化PE到L1的集群内互连吞吐量减少，难以充分利用其处理元素（PE）。 本文提出了动态分配方案（DAS），这是一种运行时可编程的地址重映射硬件单元，与统一的内存分配器相结合，旨在最小化PE对多银行L1的数据访问竞争。 我们在一个激进扩展的1024-PE RISC-V集群上评估了DAS，该集群具有非均匀内存访问（NUMA）的PE到L1互连，以展示其在大型并行机器学习工作负载中提高数据局部性的潜力。 对于Vision Transformer（ViT）-L/16模型，每个编码器层执行时间为5.67毫秒，在0.81 PE利用率下，相比固定的字级交错基线实现了1.94倍的速度提升。 在12nm FinFET技术中实现，DAS的面积开销小于0.1%。 显示英文摘要

摘要： 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 <0.1 % area overhead.