As Moore's law slows down, developers face difficult choices between low-level HDLs (Verilog, VHDL) offering fine-grained control and higher-level tools (HLS and Chisel) promising improved productivity. While high-level tools accelerate development, performance gaps persist compared to expert HDL implementations. Prior studies emphasize end-to-end performance, offering limited insight into why tools excel or where performance diverges in the design hierarchy. We introduce a hierarchical framework for comparing hardware generation tools by decomposing HPC kernels (FFT, GEMM, QR factorization) into reusable primitives (MAC arrays, butterflies, permutations, reduction trees). Across Verilog, Chisel, and Vivado HLS, we built an automated tool flow and synthesized ~1, 500 variants on AMD Alveo U250, measuring resource utilization and frequency. We derived theoretical bounds for validation. Verilog achieves the highest frequency and lowest resource usage; Chisel performs comparably (5--15% gap), while HLS shows a 20--40% gap. All tools operate within bounds for well-structured designs. Crucially, performance divergence arises during primitive assembly, indicating that high-level tools require better composition optimization. This reproducible framework provides actionable insights and is extensible to other tools, domains, and FPGA architectures.

This paper presents hardware generation for accelerating various floating-point (FP) trigonometric functions, including sine, cosine, and arctangent. The Chisel Hardware Construction Language (HCL) is used to develop parameterized and flexible designs for these functions. The hardware generator supports multiple design architectures with configurable parameters, such as precision (e.g., 16-bit, 32-bit, 64-bit, and 128-bit), iteration count, and pipeline depth, allowing for customization of hardware resource utilization, latency, speed, and accuracy.