Reinforcement learning (RL) enables autonomous decision-making in applications such as robotics and control, and Soft Actor-Critic (SAC) is a leading model-free algorithm for continuous tasks. However, SAC’s small-batch training updates and fine-grained computation lead to heavy scheduling and kernel-launch overheads on GPU, limiting efficiency. In this work, we present FHPSAC (FPGA-based High-Parallelism SAC Accelerator), the first FPGA-accelerated architecture dedicated to SAC training. First, we propose a hardware–software co-designed on-chip memory hierarchy to statically partition and allocate SAC’s training data for conflict-free parallel access. Second, we build a high-parallelism accelerator with a tensor core for GEMM (General Matrix Multiply) and a lightweight unit for irregular elementwise/reduction kernels. Finally, we implement the full system on a Xilinx XCVU9P FPGA and demonstrate significant speedup with low power. Experimental results show that FHPSAC obtains 5.33–14.85× speedup compared with the Intel Xeon Gold 6130 CPU, while outperforming an NVIDIA A100-SXM4 GPU by 2.90–10.43× in training latency with an average power of 40.17 W. FHPSAC substantially reduces SAC training latency, providing a computational foundation for large-scale SAC deployments.

Hierarchical time series data comprises a collection of time series aggregated at multiple levels based on categorical, geographical, or physical constraints, the analysis of which aids analysts across various domains like retail, finance, and energy, in gaining valuable insights and making informed decisions. However, existing interactive exploratory analysis approaches for hierarchical time series data fall short in analyzing time series across different aggregation levels and supporting more complex analytical tasks beyond common ones like summarize and compare. These limitations motivate us to develop a new visual analytics approach. We first generalize a taxonomy to delineate various tasks in hierarchical time series analysis, derived from literature survey and expert interviews. Based on this taxonomy, we develop ChronoDeck, an interactive system that incorporates a multi-column hierarchical time series visualization for implementing various analytical tasks and distilling insights from the data. ChronoDeck visualizes each aggregation level of hierarchical time series with a combination of coordinated dimensionality reduction and small multiples visualizations, alongside interactions including highlight, align, filter, and select, assisting users in the visualization, comparison, and transformation of hierarchical time series, as well as identifying the entities of interest. The effectiveness of ChronoDeck is demonstrated by case studies on three real-world datasets and expert interviews.