Accuracy Limitations in Optical Linear Algebra Processors

One of the limiting factors in applying optical linear algebra processors (OLAPs) to real-world problems has been the poor achievable accuracy of these processors. Little previous research has been done on determining noise sources from a systems perspective which would include noise generated in the multiplication and addition operations, noise from spatial variations across arrays, and from crosstalk. In this dissertation, we propose a second-order statistical model for an OLAP which incorporates all these system noise sources. We now apply this knowledge to determining upper and lower bounds on the achievable accuracy. This is accomplished by first translating the standard definition of accuracy used in electronic digital processors to analog optical processors. We then employ our second-order statistical model. Having determined a general accuracy equation, we consider limiting cases such as for ideal and noisy components. From the ideal case, we find the fundamental limitations on improving analog processor accuracy. From the noisy case, we determine the practical limitations based on both device and system noise sources. These bounds allow system trade-offs to be made both in the choice of architecture and in individual components in such a way as to maximize the accuracy of the processor. Finally, by determining the fundamental limitations, we show the system engineer when the accuracy desired can be achieved from hardware or architecture improvements and when it must come from signal pre-processing and/or post-processing techniques.