Most modern computing tasks have digital electronic input and output data. Due to these constraints imposed by real-world use cases of computer systems, any analog computing accelerator, whether analog electronic or optical, must perform an analog-to-digital conversion on its input data and a subsequent digital-to-analog conversion on its output data. The energy and latency costs incurred by data conversion place performance limits on analog computing accelerators. To avoid this overhead, 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 optical hardware. This article presents a case study that profiles 27 benchmarks for an analog optical Fourier transform and convolution accelerator which we designed and built. The case study shows that an ideal optical Fourier transform and convolution accelerator can produce an average speedup of 9.4 times and a median speedup of 1.9 times for the set of benchmarks. The optical Fourier transform and convolution accelerator only produces significant speedup for pure Fourier transform (45.3 times) and convolution (159.4 times) applications.

翻译：大多数现代计算任务使用数字电子输入和输出数据。由于计算机系统实际应用场景的限制，任何模拟计算加速器（无论是模拟电子还是光学）都必须对其输入数据进行模数转换，并对其输出数据进行数模转换。数据转换所需的能耗和延迟成本对模拟计算加速器的性能造成了限制。为避免这一开销，模拟硬件必须替代传统数字电子计算机硬件的全部功能。由于光学硬件在增益、输入输出隔离以及信息存储方面的局限性，目前光学计算加速器尚无法实现这一目标。本文以我们设计构建的模拟光学傅里叶变换与卷积加速器为案例，对27个基准程序进行了性能剖析。案例研究表明，该理想光学傅里叶变换与卷积加速器在基准测试集上可实现平均9.4倍、中位数1.9倍的加速效果，其中纯傅里叶变换应用（45.3倍）和纯卷积应用（159.4倍）的加速效果尤为显著。