Due to complex interactions among various deep neural network (DNN) optimization techniques, modern DNNs can have weights and activations that are dense or sparse with diverse sparsity degrees. To offer a good trade-off between accuracy and hardware performance, an ideal DNN accelerator should have high flexibility to efficiently translate DNN sparsity into reductions in energy and/or latency without incurring significant complexity overhead. This paper introduces hierarchical structured sparsity (HSS), with the key insight that we can systematically represent diverse sparsity degrees by having them hierarchically composed from multiple simple sparsity patterns. As a result, HSS simplifies the underlying hardware since it only needs to support simple sparsity patterns; this significantly reduces the sparsity acceleration overhead, which improves efficiency. Motivated by such opportunities, we propose a simultaneously efficient and flexible accelerator, named HighLight, to accelerate DNNs that have diverse sparsity degrees (including dense). Due to the flexibility of HSS, different HSS patterns can be introduced to DNNs to meet different applications' accuracy requirements. Compared to existing works, HighLight achieves a geomean of up to 6.4x better energy-delay product (EDP) across workloads with diverse sparsity degrees, and always sits on the EDP-accuracy Pareto frontier for representative DNNs.

翻译：摘要：由于深度神经网络（DNN）中多种优化技术间的复杂交互，现代DNN的权重和激活可能呈现密集或稀疏状态，且具有不同的稀疏度。为在精度与硬件性能间实现良好权衡，理想的DNN加速器应具备高灵活性，以高效地将DNN稀疏性转化为能耗和/或延迟的降低，同时避免引入显著复杂度开销。本文提出层级结构化稀疏（HSS），其核心洞察在于：可通过将多种简单稀疏模式分层组合，系统性地表征不同稀疏度。由此，HSS简化了底层硬件设计——仅需支持简单稀疏模式，从而大幅降低稀疏加速开销并提升效率。基于此机遇，我们提出兼具高效性与灵活性的加速器HighLight，其可加速包含多种稀疏度（包括密集）的DNN。借助HSS的灵活性，可将不同HSS模式引入DNN以满足不同应用的精度需求。与现有工作相比，HighLight在具有不同稀疏度的工作负载上实现了高达6.4倍的能效积（EDP）几何平均提升，并在代表性DNN的EDP-精度帕累托前沿上始终占据最优位置。