  Several microring resonator (MRR) based analog photonic architectures have
been proposed to accelerate general matrix-matrix multiplications (GEMMs) in
deep neural networks with exceptional throughput and energy efficiency. To
implement GEMM functions, these MRR-based architectures, in general, manipulate
optical signals in five different ways: (i) Splitting (copying) of multiple
optical signals to achieve a certain fan-out, (ii) Aggregation (multiplexing)
of multiple optical signals to achieve a certain fan-in, (iii) Modulation of
optical signals to imprint input values onto analog signal amplitude, (iv)
Weighting of modulated optical signals to achieve analog input-weight
multiplication, (v) Summation of optical signals. The MRR-based GEMM
accelerators undertake the first four ways of signal manipulation in an
arbitrary order ignoring the possible impact of the order of these
manipulations on their performance. In this paper, we conduct a detailed
analysis of accelerator organizations with three different orders of these
manipulations: (1) Modulation-Aggregation-Splitting-Weighting (MASW), (2)
Aggregation-Splitting-Modulation-Weighting (ASMW), and (3)
Splitting-Modulation-Weighting-Aggregation (SMWA). We show that these
organizations affect the crosstalk noise and optical signal losses in different
magnitudes, which renders these organizations with different levels of
processing parallelism at the circuit level, and different magnitudes of
throughput and energy-area efficiency at the system level. Our evaluation
results for four CNN models show that SMWA organization achieves up to
4.4$\times$, 5$\times$, and 5.2$\times$ better throughput, energy efficiency,
and area-energy efficiency, respectively, compared to ASMW and MASW
organizations on average.
