Had a quick read of [3]. This work comments on three contributions to the error in an analog scheme to solve linear systems via an ODE that is encoded in the circuit dynamics, although the specific ODE being solved is not given in the paper. The three sources addressed are: gain error, offset error, and nonlinearity. It is mentioned that the first two can be corrected by calibration, while the nonlinearity error can be mitigated by scaling down the inputs to the problem (the matrix A and vector b in the equation Ax = b). It says that scaling down the problem results in lower accuracy, which I suspect can be captured by the tradeoff we show analytically between time, energy, and accuracy. It is also mentioned that “when the analog accelerator outputs are steady, we can sample the solutions once with higher-precision ADCs. However, the method here does not involve time-averaging the output of the circuit. A core result of our paper is that the accuracy converges with the length of time over which the output is averaged, so I suspect that taking a single sample is a drawback of the method presented here.

The Optical Fourier Transform (OFT) in [2] is a way to compute the Discrete Fourier Transform (DFT), and is an alternative to the Fast Fourier Transform (FFT). The OFT basically does a matrix-vector multiplication, where the matrix has a special form, which is accomplished physically using the diffraction of light as it propagates from one plane to another. Although the paper claims constant time for this operation, it’s likely that when the optical array gets bigger, the light has to propagate further for the diffraction pattern to emerge, meaning the time gets longer too. But the coefficient of this scaling is extremely small, because light travels fast. This also accelerates linear algebra using physical dynamics, but can only be used to multiply one particular matrix (the DFT matrix) by an arbitrary vector.