发表机构
KU Leuven（荷语鲁汶大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CONFERM提出一种递归感知的时间映射方法，通过利用主导时间约束优化DFG表示，实现循环携带流水线，在十个基准上将吞吐量提升2.18倍，启动间隔缩短46%，收敛速度提升5.07倍。
AI 中文摘要
在DSP和机器学习工作负载中，吞吐量通常受到两种时间结构的限制，即循环携带的递归和长延迟的多周期计算节点。在时空粗粒度可重构阵列（CGRA）上，这两种瓶颈都可以通过在多上下文模配置中重叠迭代来解决。然而，现有的CGRA映射器调度固定的数据流图（DFG），将递归感知调度和算子级流水线分开处理，限制了迭代间重叠并增加了布线压力。为解决此问题，我们提出了CONFERM，一种递归感知的时间映射器，利用主导时间约束来指导DFG表示，并暴露循环携带流水线的机会。CONFERM在调度期间识别并优先处理瓶颈区域。交错迭代中规则的循环携带偏移使得发出的控制序列能够以比原始启动间隔更短的节奏重复，从而在降低CGRA配置开销的同时提供更高的吞吐量。在十个基准内核上，CONFERM相较于最先进的映射器将吞吐量提高了2.18倍。其均匀的迭代偏移将发出的启动间隔缩短了46%。CONFERM的映射器遍次在使用相同启发式映射器后端时，平均收敛速度也快了5.07倍。
英文摘要
Throughput in DSP and machine learning workloads is often limited by two temporal structures, i.e., loop-carried recurrences and long-latency, multi-cycle compute nodes. On spatio-temporal coarse-grained reconfigurable arrays (CGRAs), both bottlenecks can be addressed by overlapping iterations across the multi-context modulo configurations. Yet, existing CGRA mappers schedule a fixed dataflow graph (DFG) that treats recurrence-aware scheduling and operator-level pipelining separately, limiting inter-iteration overlap and inflating routing pressure. To tackle this, we present CONFERM, a recurrence-aware temporal mapper that uses the dominant temporal con-straint to guide the DFG representation and expose opportunities for loop-carried pipelining. CONFERM identifies and prioritizes bottleneck regions during scheduling. The regular loop-carried offsets across interleaved iterations allow the emitted control sequence to repeat at a shorter cadence than the original initiation interval, thus delivering higher throughput with lower CGRA configuration overhead. Across ten benchmark kernels, CONFERM improves throughput by 2.18x over state-of-the-art mappers. Its uniform iteration offsets shorten the emitted initiation interval by 46%. CONFERM's mapper pass also converges faster by 5.07x on average with the same heuristic mapper backend.
CommentsAccepted by ICCD 2026, 16-18 November 2026, Hong Kong, China
