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High-Speed and Energy-Efficient Non-Binary Computing with Polymorphic Electro-Optic Circuits and Architectures

arXiv 2304.07608 ↗First posted 15 Apr 2023cs.AR cs.AI cs.LGcited by 1

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Abstract (arXiv)

In this paper, we present microring resonator (MRR) based polymorphic E-O circuits and architectures that can be employed for high-speed and energy-efficient non-binary reconfigurable computing. Our polymorphic E-O circuits can be dynamically programmed to implement different logic and arithmetic functions at different times. They can provide compactness and polymorphism to consequently improve operand handling, reduce idle time, and increase amortization of area and static power overheads. When combined with flexible photodetectors with the innate ability to accumulate a high number of optical pulses in situ, our circuits can support energy-efficient processing of data in non-binary formats such as stochastic/unary and high-dimensional reservoir formats. Furthermore, our polymorphic E-O circuits enable configurable E-O computing accelerator architectures for processing binarized and integer quantized convolutional neural networks (CNNs). We compare our designed polymorphic E-O circuits and architectures to several circuits and architectures from prior works in terms of area, latency, and energy consumption.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 34 references that are in the map. First posted on arXiv 15 Apr 2023.

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10 of 34 references.

References · 10

  1. 2020Photonics for artificial intelligence and neuromorphic computingcited by 111
  2. 2021Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimizationcited by 57
  3. 2019Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)cited by 22
  4. 2021ROBIN: A Robust Optical Binary Neural Network Acceleratorcited by 15
  5. 2022Photonic Reconfigurable Accelerators for Efficient Inference of CNNs with Mixed-Sized Tensorscited by 5
  6. 2021Silicon Photonic Microring Based Chip-Scale Accelerator for Delayed Feedback Reservoir Computingcited by 4
  7. 2023SCONNA: A Stochastic Computing Based Optical Accelerator for Ultra-Fast, Energy-Efficient Inference of Integer-Quantized CNNscited by 4
  8. 2023An Optical XNOR-Bitcount Based Accelerator for Efficient Inference of Binary Neural Networkscited by 3
  9. 2020PROTEUS: Rule-Based Self-Adaptation in Photonic NoCs for Loss-Aware Co-Management of Laser Power and Performancecited by 2
  10. 2023A Bit-Parallel Deterministic Stochastic Multipliercited by 2

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