This paper presents a low-latency coordinate rotation digital computer (CORDIC) algorithm to accelerate the computation of arctangent functions, and it describes the corresponding iterative and pipelined architecture of this novel algorithm. As compared to the existing methods based on CORDIC, the proposed method can effectively reduce the number of iterations by dedicated pre-rotation and comparison processes. Moreover, the proposed CORDIC algorithm supports all vectors with arbitrary angles while maintaining convergence. By error analysis, the proposed algorithm can achieve the same accuracy as the conventional CORDIC algorithm during floating-point arctangent function computation and reduce the number of iterations by approximately 50%. This paper presents two new architectures—the iterative architecture, which can be more resource efficient, and the pipelined architecture, which can achieve a throughput rate of one datum per clock. Finally, the experimental comparison results indicate that the proposed method outperforms extant methods as it exhibits low latency, requires fewer resources to compute the arctangent function for floating-point inputs, and necessitates no digital signal processing (DSP) and memory for fixed-point inputs.

This brief presents a novel pipeline architecture designed to compute the fast Fourier transform (FFT) on real input signals in a serial format. This architecture significantly improves resource efficiency by sharing adders between butterfly and rotator structures. In addition, a novel data management approach for N-point radix-2 serial real-valued FFT (RFFT) has been proposed, which not only simplifies the data reordering circuit between processing elements (PEs) but also achieves natural order data output. The real-valued 1024-point FFT has been implemented on a field-programmable gate array (FPGA). Compared with typical real-valued serial commutator (RSC) FFT architecture, the proposed architecture achieves substantial improvement, including a reduction of 10.3% in the number of lookup tables (LUTs) and 12.5% in flip-flops (FFs).

This brief presents a pipelined floating-point Multiply–Accumulator (FPMAC) architecture designed to accelerate sparse linear algebra operations. By designing a lookup-table-based 5–3 carry-save adder (CSA) and combining it with a 3–2 CSA, the proposed design minimizes the critical path and boosts operational speed. Moreover, the proposed architecture takes advantage of data characteristics in sparse linear algebra to displace the shift unit in the critical accumulation loop, further increasing the throughput rate. In addition, the integration of a lookup-table-based leading-zero anticipator (LZA) enhances normalization efficiency. Experimental results show that, compared with reported FPMAC designs, the proposed architecture may achieve a significantly higher maximum clock frequency for single-precision floating-point operations.