2023/02/14 by Sairam Sri Vatsavai, Vatsavai, Sairam Sri, Venkata Sai Praneeth Karempudi +7
Computer Science · Engineering · #Advanced Memory and Neural Computing #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Computation #Computational science #Computer engineering #Computer hardware #Computer science #Convolutional neural network #Efficient energy use #Electrical engineering #Electronic engineering #Emerging Technologies (cs.ET) #Engineering #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Hardware acceleration #Inference #Machine Learning (cs.LG) #Multiplexing #Neural Networks and Reservoir Computing #Optical Network Technologies #Optics #Parallel computing #Photonics #Physics #Stochastic computing #Telecommunications #Throughput
paper · pdf · doi:10.48550/arxiv.2302.07036
openalex publication_date 2023/02/14 · openalex created_date 2023/02/17 · openalex updated_date 2026/08/06
The acceleration of a CNN inference task uses convolution operations that are typically transformed into vector-dot-product (VDP) operations. Several photonic microring resonators (MRRs) based hardware architectures have been proposed to accelerate integer-quantized CNNs with remarkably higher throughput and energy efficiency compared to their electronic counterparts. However, the existing photonic MRR-based analog accelerators exhibit a very strong trade-off between the achievable input/weight precision and VDP operation size, which severely restricts their achievable VDP operation size for the quantized input/weight precision of 4 bits and higher. The restricted VDP operation size ultimately suppresses computing throughput to severely diminish the achievable performance benefits. To address this shortcoming, we for the first time present a merger of stochastic computing and MRR-based CNN accelerators. To leverage the innate precision flexibility of stochastic computing, we invent an MRR-based optical stochastic multiplier (OSM). We employ multiple OSMs in a cascaded manner using dense wavelength division multiplexing, to forge a novel Stochastic Computing based Optical Neural Network Accelerator (SCONNA). SCONNA achieves significantly high throughput and energy efficiency for accelerating inferences of high-precision quantized CNNs. Our evaluation for the inference of four modern CNNs at 8-bit input/weight precision indicates that SCONNA provides improvements of up to 66.5x, 90x, and 91x in frames-per-second (FPS), FPS/W and FPS/W/mm2, respectively, on average over two photonic MRR-based analog CNN accelerators from prior work, with Top-1 accuracy drop of only up to 0.4% for large CNNs and up to 1.5% for small CNNs. We developed a transaction-level, event-driven python-based simulator for the evaluation of SCONNA and other accelerators (https://github.com/uky-UCAT/SCONNSIM.git).