We propose a novel, error-efficient approximate multiplier (EEAM), which is based on a rounding-based approach (RBA). Multiplication is performed using rounding, shift, and add operations. We round the input operands to the nearest power of two using RBA. The modified inputs are processed by an arithmetic block (AB), which consists of addition, subtraction, and shifter blocks. The proposed approximate multiplier has input operands whose widths range from 8-bit to 32-bits. We simulated the proposed multiplier by using Vivado and MATLAB. The proposed multiplier is also synthesized using the Cadence RTL compiler, and compared to prior approximate multiplier proposals, EEAM’s delay and energy consumption are about of 22% and 57% better than the best known approximate multipliers. We also show that the proposed approximate multiplier’s worst-case error, mean error distance, mean relative error distance, and normalized error distance are about 3%, 44%, 45%, and 13% improvement over existing approximate multipliers. Finally, we used the proposed approximate multiplier in an image smoothing filter., For this application, we observed that our multiplier provides higher PSNR and SSIM than any prior approximate multiplier.

我们提出了一种新颖的、错误高效的近似乘法器 (EEAM)，它基于基于舍入的方法 (RBA)。乘法使用舍入、移位和加法运算来执行。我们使用 RBA 将输入操作数四舍五入到最接近的 2 的幂。修改后的输入由算术块 (AB) 处理，该块由加法、减法和移位器块组成。建议的近似乘法器具有输入操作数，其宽度范围从 8 位到 32 位。我们使用 Vivado 和 MATLAB 模拟了建议的乘法器。提议的乘法器也是使用 Cadence RTL 编译器合成的，与之前的近似乘法器提议相比，EEAM 的延迟和能耗比最知名的近似乘法器分别好 22% 和 57%。我们还表明，所提出的近似乘法器的最坏情况误差，平均误差距离、平均相对误差距离和归一化误差距离比现有的近似乘数提高了大约 3%、44%、45% 和 13%。最后，我们在图像平滑滤波器中使用了建议的近似乘法器。对于此应用，我们观察到我们的乘法器比任何先前的近似乘法器提供更高的 PSNR 和 SSIM。