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. To avoid this the analog hardware would need to completely replace the full functionality of traditional digital electronic computer hardware. Using 27 empirically-measured benchmarks 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 maximum speedups achieved were 45.3 times for a pure Fourier transform and 159.4 times for a pure convolution. These results show that an optical accelerator only produces significant speedup for applications consisting exclusively of Fourier transforms and convolutions. In addition to the theoretical results we quantify the data movement bottleneck which causes a 23.8 times slowdown in a prototype optical Fourier transform accelerator which we built from widely-available off-the-shelf parts.

翻译：现代计算任务受限于数字电子输入与输出数据。由于用户施加的这些约束，任何模拟计算加速器必须对其输入数据进行模数转换，并随后对其输出数据进行数模转换。若要避免这一环节，模拟硬件需完全取代传统数字电子计算机硬件的全部功能。通过27项经验测量基准测试，我们估算得出：理想光学加速器在加速傅里叶变换与卷积时，平均可实现9.4倍的加速比，中位加速比为1.9倍。纯傅里叶变换与纯卷积分别达到45.3倍与159.4倍的最大加速比。这些结果表明，光学加速器仅对完全由傅里叶变换与卷积组成的应用产生显著加速效果。除理论结果外，我们量化了数据移动瓶颈——该瓶颈导致我们采用商用现成部件构建的光学傅里叶变换加速器原型出现23.8倍的性能衰减。