<i>HEANA</i> : A Hybrid Time-Amplitude Analog Optical Accelerator with Flexible Dataflows for Energy-Efficient CNN Inference

Several photonic microring resonator (MRR)-based analog accelerators have been proposed to accelerate the inference of integer-quantized Convolutional Neural Networks (CNNs) with remarkably higher throughput and energy efficiency compared to their electronic counterparts. However, the existing analog photonic accelerators suffer from three shortcomings: (1) severe hampering of wavelength parallelism due to various crosstalk effects, (2) inflexibility of supporting various dataflows with temporal accumulations, and (3) failure in fully leveraging the ability of photodetectors to perform in situ accumulations. These shortcomings collectively hamper the performance and energy efficiency of prior accelerators. To tackle these shortcomings, we present a novel H ybrid tim E - A mplitude a N alog optical A ccelerator, called HEANA. HEANA employs hybrid time-amplitude analog optical modulators (TAOMs) in a spectrally hitless arrangement, which significantly reduces optical signal losses and crosstalk effects, thereby increasing the wavelength parallelism in HEANA. HEANA employs our invented balanced photo-charge accumulators (BPCAs) that enable buffer-less, in situ, spatio-temporal accumulations to eliminate the need to use reduction networks in HEANA, relieving it from related latency and energy overheads. Moreover, TAOMs and BPCAs increase the flexibility of HEANA to efficiently support spatio-temporal accumulations for various dataflows. Our evaluation for the inference of four modern CNNs indicates that HEANA provides improvements of at least 25× and 32× in frames per second (FPS) and FPS/W (energy efficiency), respectively, for equal-area comparisons on gmean over two MRR-based analog CNN accelerators from prior work.