Most modern computing tasks are constrained to having digital electronic input and output data. Due to these constraints imposed by the user, any analog computing accelerator must perform an analog-to-digital conversion on its input data and a subsequent digital-to-analog conversion on its output data. This places performance limits on analog computing accelerator hardware. To avoid this, analog hardware must replace the full functionality of traditional digital electronic computer hardware. This is not currently possible for optical computing accelerators due to limitations in gain, input-output isolation, and information storage in current optical hardware. In our case study we profiled 27 benchmarks on an analog optical Fourier transform and convolution accelerator. We estimate that an ideal optical accelerator that accelerates Fourier transforms and convolutions can produce an average speedup of 9.4 times, and a median speedup of 1.9 times for the set of benchmarks. The case study shows that the optical Fourier transform and convolution accelerator only produces significant speedup for applications consisting exclusively of Fourier transforms (45.3 times) and convolutions (159.4 times).

翻译：大多数现代计算任务受限于数字电子输入和输出数据。由于用户施加的这些约束，任何模拟计算加速器都必须对其输入数据进行模数转换，并对其输出数据进行后续的数模转换。这给模拟计算加速器的硬件性能带来了限制。为避免这一瓶颈，模拟硬件必须替代传统数字电子计算机硬件的全部功能。目前，由于当前光学硬件在增益、输入输出隔离和信息存储方面的局限性，光学计算加速器尚无法实现这一点。在我们的案例研究中，我们对一个模拟光学傅里叶变换与卷积加速器上的27个基准测试进行了性能剖析。我们估算，一个理想的、能够加速傅里叶变换和卷积的光学加速器，对于该基准测试集，平均可产生9.4倍的加速比，中位加速比为1.9倍。该案例研究表明，光学傅里叶变换与卷积加速器仅对完全由傅里叶变换（45.3倍）和卷积（159.4倍）组成的应用产生显著加速效果。