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An Optimized Floating-Point Matrix Multiplication on FPGA

机译:FPGA上的优化浮点矩阵乘法

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Matrix multiplication is a kernel and fundamental operation in many applications including image, robotic and digital signal processing. The key component of matrix multiplication is Multiplier Accumulator (MAC) which is a decisive component for the performance of matrix multiplication. This study proposes a pipelined floating-point MAC architecture on Field Programmable Gate Array (FPGA) using a novel accumulating method. By adding the last N-stage results of the pipelined adder, the accumulation of the multiplier products can be obtained. Then, a matrix multiplication is implemented by employing parallel systolic structure based on the proposed MAC. Experimental results demonstrate that the proposed MAC architecture achieves higher clock speed and consumes less hardware resources than previous designs and the matrix multiplier has a good performance and scalability. It also can be concluded that the efficiency of the matrix multiplier is even higher when the matrices are larger.
机译:矩阵乘法是许多应用(包括图像,机械手和数字信号处理)中的内核和基本操作。矩阵乘法的关键组件是乘法累加器(MAC),它是矩阵乘法性能的决定性组件。这项研究提出了一种使用新型累加方法的现场可编程门阵列(FPGA)上的流水线浮点MAC体系结构。通过将流水线加法器的最后N个阶段的结果相加,可以获得乘积的累加。然后,基于所提出的MAC,通过采用并行脉动结构来实现矩阵乘法。实验结果表明,与以前的设计相比,所提出的MAC体系结构可实现更高的时钟速度并消耗更少的硬件资源,并且矩阵乘法器具有良好的性能和可扩展性。还可以得出结论,当矩阵更大时,矩阵乘法器的效率甚至更高。

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