  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.
