qGAMMA：面向多精度加速器的DNN映射优化框架
qGAMMA: A Framework for Optimal DNN Mapping on Multi-Precision Accelerators
做硬件加速或模型部署的朋友可以看看，qGAMMA 用遗传算法帮你自动找混合精度模型在加速器上的最优映射方案。
qGAMMA 是一个用于混合精度深度神经网络映射到空间架构加速器的优化框架。它采用领域专用遗传算法搜索设计空间，并结合基于硬件综合结果的分析性能模型。
该框架能同时找出最优的模型映射方案和加速器配置，以最小化性能、能耗或资源利用率等目标指标。论文在多个 DNN 工作负载上完成评估，展示了在性能、能效与资源利用之间取得更好权衡的可行性。
qGAMMA: A Framework for Optimal DNN Mapping on Multi-Precision Accelerators
This work describes qGAMMA, a flexible framework for exploring and optimizing the mapping of mixed-precision Deep Neural Network (DNN) models onto general-purpose spatial accelerators that support a range of fixed- and floating-point precisions. qGAMMA navigates the design space using a domain-specific genetic algorithm and an analytical performance model based on hardware synthesis results. The framework identifies the optimal mapping and accelerator configuration that minimizes target metrics. Extensive evaluations across DNN workloads show that qGAMMA offers valuable insights for designing mixed-precision applications, enabling better trade-offs between performance, energy efficiency, and resource utilization.
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include qGAMMA, 混合精度, DNN加速器, 遗传算法.
Source attribution: curated from arXiv cs.AI (@Leandro Fiorin, Luigi Altamura, Cristina Silvano).