Introduction
Optical linear algebra processors (OLAPs) perform numerical calculations such as matrix–vector multiplication by equating a particular property of light, normally its intensity level, to a number. Through various effects to be discussed in this section, uncertainty is introduced either through random fluctuations or the addition of a bias. The fluctuating intensity can now be considered a random variable for which a mean and standard deviation can be determined. After corrections for bias, the mean value can be taken as the correct level that relates to the expected numerical value while the nonzero standard deviation represents processor noise.
In the design of analog OLAPs such as matrix–vector and matrix–matrix processors, the effect of noise from both device and system sources has a major impact on the choice of system architecture and components. The ability of a particular system design to meet system performance specifications such as signal-to-noise ratio (SNR), dynamic range, and accuracy is directly connected to the noise properties of the system and its components. As an example, the choice of the spatial light modulator (SLM) has a significant impact (Casasent & Ghosh, 1985; Perle & Casasent, 1990; Taylor & Casasent, 1986; Batsell, Jong, Walkup & Krile, 1989a, 1990; Jong, Walkup & Krile, 1986). This chapter examines the effects of noise on these system specifications and their implications for OLAP design.
We begin with a theoretical analysis of a simple matrix–vector multiplier and then generalize the results for N cascaded matrix–vector multipliers.